data engineer

senior data scientist

senior data engineer

analytics engineer

lead data scientist

data scientist

BigHand

<div><h5 class="jobSectionHeader">About BigHand</h5> 
BigHand offers technology solutions for busy legal professionals that provide data and insights to increase value and profits. Through our expert knowledge and innovative technology, we enable our customers to achieve enhanced efficiency, better decision-making and harness their competitive edge.
We build with tomorrow in mind, transforming ideas into action and empowering our customers to succeed in a world of constant change. We make big happen for our customers.
We are a business driven by our values and have these at the centre of everything we do. Our BigHanders are dedicated to building a culture where We Win Together, We Love to Learn, We Do the Right Thing, We Own It and We Find the Fun.<h5 class="jobSectionHeader"> 
About the role</h5> 
BigHand builds software that legal organisations rely on every day. We are looking for a Data Scientist to help us design and ship products to establish our partnership with Ayora, where a clean, considered user interface meets genuinely intelligent behaviour underneath.<div>
This is a hands-on role for a data scientist who wants their work in production rather than in a notebook. You will build and evaluate the models and LLM systems that sit behind the product, contribute to how problems are framed as well as how they are solved, and take real ownership of the quality of what we release.</div><h5 class="jobSectionHeader"> 
What you&rsquo;ll do 
 
</h5><ul><li>Train, tune and evaluate models, and prepare them for production deployment alongside engineering.</li><li>
Build and improve LLM and agentic AI systems, including harnesses, retrieval augmented generation, context engineering and evaluation frameworks.</li><li>
Wrangle and analyse messy real-world legal data, and turn it into something the product can rely on.</li><li>
Design evaluations that tell us honestly whether a model or agent is working, and act on what they show.</li><li>
Explain complex statistical and machine learning concepts and results clearly, through visualisation and summarisation, including to non-technical audiences.</li><li>
Contribute to technical discussion and help refine requirements rather than simply consuming them.</li><li>
Deliver consistently within a collaborative agile framework, with a track record of predictable, high quality output.</li><li>
Develop well-constructed solutions using the best tools available, avoiding fragile shortcuts taken for short-term speed or to sidestep discussion.</li><li>
Apply modern software development lifecycle practices, including code review, testing and continuous delivery.</li></ul><h5 class="jobSectionHeader"> 
What we&rsquo;re looking for 
 
</h5><ul><li>An analytical background, evidenced by a STEM degree in a technical subject. Physical sciences or mathematics are preferred.</li><li>
Strong Python skills and competency across the Python data science ecosystem such as pandas, numpy and scikit-learn, or a highly transferable equivalent.</li><li>
Experience working with LLMs and agentic AI systems, including harnesses, RAG, context engineering and evaluations.</li><li>
Experience working safely and efficiently with agentic engineering systems.</li><li>
Solid experience with data wrangling and data analysis.</li><li>
The ability to train, tune and evaluate models and prepare them for production deployment.</li><li>
A solid understanding of modern software development lifecycle practices.</li><li>
The ability to navigate typical problems independently, contribute to discussion and help shape technical requirements.</li><li>
The ability to communicate complex statistical and machine learning concepts and their results clearly, including to non-technical audiences.</li></ul><div><div>
Desirable<ul><li>
An insatiable appetite for learning and for acquiring new knowledge, skills and experience.</li><li>
Comfort operating independently on both existing and new systems while preserving and improving a consistent project structure.</li><li>
A positive force in building the team as we grow and develop into more senior positions.</li><li>
A habit of introducing new technology and practices that accelerate the team, for example through lunch and learn sessions.</li><li>
Experience in the legal industry or another regulated environment such as health or financial services.</li></ul></div></div><h5 class="jobSectionHeader"> 
What we offer</h5><ul><li>
25 days holiday (exc. bank holidays) + 3 BigHand days off between Christmas &amp; New Year</li><li>
Various wellbeing benefits including private medical, vision, online wellbeing programs &amp; webinars, access to online therapy and Employee Resource Groups (ERG)</li><li>
Company-wide Wellness day, for our teams all across the globe to switch off and spend the day doing something to nourish their mental health</li><li>
Opportunities to give back with company volunteer events and individual volunteer days</li><li>
Career growth opportunities and study leave allowance</li><li>
Family benefits including enhanced parental leave, secondary caregiver leave and fertility support</li><li>
Financial benefits including pension scheme, life assurance, and salary sacrificing</li><li>
Cycle to Work Scheme and Tech Scheme</li><li>
Growing business with a global presence, flexible working, modern offices, and remote work options</li></ul><h5 class="jobSectionHeader">
Our DEI Commitment</h5>
We welcome the unique value and individuality that you can bring to BigHand. We are an equal opportunity employer and are dedicated to recruiting solely based on capability and potential regardless of race, gender, sexual orientation, gender identity or expression, age, educational background, national origin, religion, military status or physical ability. Our BigHanders are dedicated to advancing our culture of belonging by embracing differing viewpoints, opinions, thoughts, and ideas.</div>

Data Scientist

Claims Recruitment Services Limited

<div><div><h3 class="jobSectionHeader">Job Information</h3>
<ul><li>Date Opened 26/08/2026
</li><li>Job Type Permanent
</li><li>Industry Insurance
</li><li>City London
</li><li>Province London
</li><li>Country United Kingdom
</li><li>Postal Code N/A
</li></ul></div><div><h3 class="jobSectionHeader">Job Description</h3>
<div><div>We are recruiting for a Senior Data Scientist on behalf of a market leading Lloyd's Syndicate.</div> 
The successful candidate will strengthen the business's data science capability by delivering models and insights that improve underwriting profitability, automation and efficiency. It is a true end-to-end role, owning projects from problem framing through to deployment and ongoing model performance, with strong object-oriented programming, machine learning and statistical skills, and experience taking models into production, designing ML pipelines and working with MLOps, CI/CD and Azure. 
Key Responsibilities<div> 
Delivery of data science products 
</div><ul><li>
Leading data science projects end-to-end, from problem framing through development, deployment and ongoing monitoring in production. 
</li><li>
Working alongside the actuarial team and coordinating with the Data Science and Data Analytics Manager to support the business with proactive analytics and insights. 
</li><li>
Using generative AI to enrich insight and unlock new opportunities, deployed and maintained through the same MLOps patterns applied to traditional models. 
</li></ul><div>
Engineering and MLOps standards 
</div><ul><li>
Designing, building and maintaining machine learning pipelines in a cloud environment, applying sound software engineering practice. 
</li><li>
Owning deployed models in life, monitoring performance and drift, and ensuring models are documented and explainable to a standard appropriate for a regulated environment. 
</li></ul><div>
Stakeholder engagement and requirements 
</div><ul><li>
Working with technical and non-technical stakeholders to identify, document, analyse and prioritise requirements for data science products. 
</li><li>
Producing clear deliverables and communicating findings and their limitations to audiences without a technical background. 
</li></ul><div>
Team and capability building 
</div><ul><li>
Coaching and upskilling data scientists and data analysts through code review, pairing and mentoring, and contributing to the data science backlog and roadmap. 
</li></ul><div>
Key Requirements</div><div></div><div> 
Essential 
</div><ul><li>
Strong Python, written to production standard, with object-oriented design and software engineering fundamentals: version control, code review, automated testing, dependency and environment management. 
</li><li>
Machine learning across the standard toolkit (scikit learn, pandas, NumPy, statsmodels or equivalents), with sound judgement about model selection, validation and the limits of what the data supports. 
</li><li>
MLOps and CI/CD in practice, covering pipeline orchestration, model versioning, automated deployment, monitoring and retraining. 
</li><li>
Cloud based machine learning delivery, ideally on Azure (Azure ML, Azure DevOps), with equivalent AWS or GCP experience considered. 
</li><li>
SQL and relational data modelling, with the ability to work efficiently against large datasets. 
</li><li>
Statistical foundations sufficient to design sound experiments, quantify uncertainty and challenge conclusions that the data does not support. 
</li></ul><div>
Desirable 
</div><ul><li>
Insurance experience (specifically in the Lloyd's market), pricing or underwriting in a regulated environment. 
</li><li>
Practical experience deploying generative AI or LLM based solutions.</li></ul> 
Altogether, this role suits a hands-on data scientist who can own the full lifecycle from problem framing to production and translate that work into commercial value alongside underwriters and actuaries. 
We do not retain any personal information without your permission. Please refer to our data protection policy on our official website for more information.</div></div></div>

Senior Data Scientist

Elsevier

<div>
 
 
 About the team
 
 
 
 Elsevier&rsquo;s mission is to help researchers, clinicians, and life sciences professionals advance discovery and improve health outcomes through trusted content, data, and analytics.
 
 
 
 
 
 This role sits within Elsevier&rsquo;s
 
 Platform Data Science organization
 
 , a centralized AI and data science group responsible for advancing intelligent discovery, retrieval, and generative AI capabilities across Elsevier products and platforms. The organization develops foundational AI technologies that power experiences such as
 
 LeapSpace
 
 , Elsevier&rsquo;s AI-powered research assistant, as well as Elsevier&rsquo;s broader
 
 Search &amp; AI Platform
 
 .
 
 
 
 
 
 The Platform Data Science organization works at the intersection of:
 
 <ul>
 <li>
 Search and retrieval systems
 </li>
 <li>
 Generative AI and LLM applications
 </li>
 <li>
 AI evaluation and experimentation
 </li>
 <li>
 Semantic enrichment and knowledge systems
 </li>
 <li>
 Scalable AI platforms and intelligent workflows
 </li>
 </ul>
 
 
 
 
 
 About the role
 
 
 
 We are looking for a
 
 Data Scientist III
 
 to help design, build, and evaluate advanced AI capabilities supporting LeapSpace and Elsevier&rsquo;s Search &amp; AI Platform initiatives. This role focuses on
 
 applied AI development, retrieval systems, and AI evaluation
 
 , helping bring cutting-edge AI technologies into production experiences used by researchers worldwide.
 
 
 
 
 
 You will work closely with senior data scientists, engineers, product managers, and domain experts across
 
 retrieval systems, generative AI, reasoning workflows, evaluation frameworks, and experimentation
 
 , contributing to the next generation of AI-powered scientific discovery tools.
 
 
 
 
 
 This role is ideal for someone with hands-on experience in
 
 applied AI, NLP, information retrieval, and LLM-based applications
 
 , who enjoys building innovative solutions and translating emerging AI techniques into impactful product capabilities.
 
 
 
 
 
 
 Key responsibilities
 
 
 
 
 Applied AI &amp; Research
 
 
 
 
 
 <ul>
 <li>
 Develop and improve
 
 LLM-powered research workflows
 
 , including:Scientific question answeringLiterature summarizationSemantic exploration and discoveryResearch insight generationCitation-aware retrieval and reasoning workflows
 </li>
 <li>
 Build and iterate on
 
 agentic and multi-step AI workflows
 
 using frameworks such as
 
 LangGraph
 
 and related orchestration tools.
 </li>
 <li>
 Apply modern techniques in:NLPGenerative AIEmbeddings and semantic representationsRetrieval-augmented generation (RAG)AI reasoning and workflow orchestration
 </li>
 <li>
 Evaluate emerging AI models, tools, and frameworks and contribute recommendations for experimentation and adoption.
 </li>
 <li>
 Contribute to prompt engineering, grounding strategies, context management, and hallucination mitigation efforts.
 </li>
 <li>
 Support integration of scientific metadata, ontologies, and knowledge assets into AI-powered workflows.
 </li>
 </ul>
 
 
 
 
 
 Search, Retrieval &amp; RAG Systems
 
 
 <ul>
 <li>
 Design, develop, and optimize
 
 search and retrieval pipelines
 
 , including lexical, vector, and hybrid retrieval approaches.
 </li>
 <li>
 Contribute to the development and enhancement of
 
 RAG systems
 
 that integrate LLMs with trusted scientific and biomedical content.
 </li>
 <li>
 Experiment with embeddings, re-ranking models, chunking strategies, and retrieval orchestration techniques to improve relevance and answer quality.
 </li>
 <li>
 Support development of semantic search, ranking, and knowledge discovery capabilities.
 </li>
 <li>
 Collaborate with engineering teams to deploy and scale AI-powered solutions.
 </li>
 </ul>
 
 
 
 
 
 AI Evaluation &amp; Experimentation
 
 
 <ul>
 <li>
 Develop and apply
 
 evaluation frameworks
 
 for search and AI systems, including:IR metrics (e.g., NDCG, recall, precision)LLM and RAG evaluation metrics (e.g., grounding, faithfulness, hallucination detection)
 </li>
 <li>
 Build and maintain evaluation datasets, benchmark suites, and annotation workflows.
 </li>
 <li>
 Conduct offline experiments and contribute to online experimentation and A/B testing.
 </li>
 <li>
 Analyze experimental results and communicate findings to stakeholders.
 </li>
 <li>
 Contribute to responsible AI practices focused on quality, reliability, and trust.
 </li>
 </ul>
 
 
 
 
 
 Cross-functional Collaboration
 
 
 <ul>
 <li>
 Partner with product managers, engineers, UX researchers, and domain experts to deliver AI-powered capabilities.
 </li>
 <li>
 Communicate technical findings and recommendations clearly to both technical and non-technical audiences.
 </li>
 <li>
 Contribute to knowledge sharing and adoption of best practices across the Platform Data Science organization.
 </li>
 <li>
 Support delivery of projects from research and experimentation through production deployment.
 </li>
 </ul>
 
 
 
 
 
 Required qualifications
 
 
 <ul>
 <li>
 
 Batchelor's, Master&rsquo;s or PhD
 
 in Computer Science, Data Science, Machine Learning, NLP, Information Retrieval, or a related field
 </li>
 <li>
 
 Experience
 
 in data science, machine learning, applied NLP, information retrieval, generative AI, or a related field
 </li>
 <li>
 Hands-on experience with:
 
 LLM-based applications and generative AI systemsRAG pipelines and retrieval systemsSearch and retrieval architectures
 
 (lexical, vector, hybrid)
 
 Evaluation methodologies
 
 for IR and generative AI systems
 </li>
 <li>
 Strong programming skills in
 
 Python
 
 </li>
 <li>
 Experience with modern AI/ML frameworks and tooling (e.g., PyTorch, Hugging Face, LangChain,
 
 LangGraph
 
 , Haystack)
 </li>
 <li>
 Experience working with
 
 Databricks
 
 or similar distributed data and machine learning platforms
 </li>
 <li>
 Understanding of experimentation methodologies, evaluation frameworks, and statistical analysis
 </li>
 <li>
 Proficiency with
 
 data visualization and analytical tooling
 
 (e.g., Tableau, Power BI, matplotlib, seaborn)
 </li>
 <li>
 Demonstrated ability to independently execute technical projects and contribute to cross-functional initiatives
 </li>
 </ul>
 
 
 
 
 
 Preferred qualifications
 
 
 <ul>
 <li>
 Experience building
 
 AI assistants, agentic workflows, or conversational AI applications
 
 </li>
 <li>
 Experience working on
 
 search, ranking, recommendation, or retrieval systems
 
 </li>
 <li>
 Familiarity with
 
 scientific, biomedical, or scholarly datasets
 
 </li>
 <li>
 Experience with
 
 knowledge graphs, ontologies, or semantic enrichment systems
 
 </li>
 <li>
 Exposure to
 
 production ML systems and MLOps practices
 
 </li>
 <li>
 Academic or industry research experience in NLP, information retrieval, search, or generative AI
 </li>
 <li>
 Experience working in content-rich, knowledge-intensive, or highly regulated domains
 </li>
 </ul>
 
 
 
 
 
 Working for you
 
 
 
 We know that your well-being and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:
 
 <ul>
 <li>
 Comprehensive Pension Plan
 </li>
 <li>
 Home, office, or commuting allowance.
 </li>
 <li>
 Generous vacation entitlement and option for sabbatical leave
 </li>
 <li>
 Maternity, Paternity, Adoption and Family Care leave
 </li>
 <li>
 Flexible working hours
 </li>
 <li>
 Personal Choice budget
 </li>
 <li>
 Internal communities and networks
 </li>
 <li>
 Various employee discounts
 </li>
 <li>
 Recruitment introduction reward
 </li>
 <li>
 Employee Assistance Program (global)
 </li>
 </ul>
 
 
 
 
 
 About the business
 
 
 
 As a global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine quality information and vast data sets with analytics to support visionary science and research, health education, and interactive learning, as well as exceptional healthcare and clinical practice. At Elsevier, your work contributes to the world&rsquo;s grand challenges and a more sustainable future. We harness innovative technologies to support science and healthcare to partner for a better world.
 
 
 
 
</div>

Data Scientist - LeapSpace

<div>
 
 
 Senior Data Scientist, London/Oxford hybrid working.
 
 
 
 
 
 
 
 Are you excited by the opportunity to design advanced AI systems that accelerate scientific discovery and unlock knowledge at scale?
 
 
 
 
 Would you enjoy building production-ready solutions using machine learning, NLP, and generative AI to create meaningful impact for researchers and professionals?
 
 
 
 
 
 
 
 About our Team
 
 
 
 Our global team support products education electronic health records that introduce students to digital charting and prepare them to document care in today&rsquo;s modern clinical environment. We have a very stable product that we&rsquo;ve worked to get to and strive to maintain. Our team values trust, respect, collaboration, agility, and quality.
 
 
 
 
 
 
 About the Role
 
 
 
 In this role, you will design and deliver advanced AI, NLP, and generative AI solutions that power knowledge discovery and decision support. You will work with complex scientific data and apply modern machine learning and LLM-based approaches to build scalable, reliable systems with real user impact. You will also collaborate across teams to turn complex challenges into practical, production-ready solutions.
 
 
 
 
 
 
 Responsibilities
 
 
 <ul>
 <li>
 Design, build, and evaluate advanced AI/ML, NLP, and generative AI solutions for scientific and knowledge-discovery applications.
 </li>
 <li>
 Develop LLM-powered workflows and retrieval-augmented generation (RAG) systems for search, summarization, question answering, and evidence-grounded insight generation.
 </li>
 <li>
 Build intelligent retrieval, ranking, recommendation, and decision-support capabilities using modern orchestration frameworks and AI techniques.
 </li>
 <li>
 Integrate scientific metadata, ontologies, taxonomies, and knowledge assets into scalable AI workflows.
 </li>
 <li>
 Establish robust evaluation, experimentation, and monitoring frameworks to ensure quality, trust, performance, and reliability.
 </li>
 <li>
 Write production-ready Python code and partner with engineering teams to deploy solutions at scale.
 </li>
 <li>
 Provide technical leadership and mentoring to support high-quality delivery and continuous improvement.
 </li>
 </ul>
 
 
 
 
 
 Requirements
 
 
 <ul>
 <li>
 Practical experience in data science, AI, machine learning, NLP, information retrieval, or a related quantitative field.
 </li>
 <li>
 Strong hands-on experience building AI/ML, NLP, generative AI, and retrieval-based systems in applied or product-focused environments.
 </li>
 <li>
 Expertise working with LLMs, including fine-tuning, prompt engineering, grounding strategies, and responsible AI practices.
 </li>
 <li>
 Strong Python skills and solid machine learning fundamentals.
 </li>
 <li>
 Experience working with large-scale text or content-rich datasets and modern AI/ML frameworks.
 </li>
 <li>
 Experience with RAG, semantic, vector, or hybrid search, along with experimentation and evaluation approaches that measure user impact.
 </li>
 <li>
 Familiarity with cloud platforms and modern software engineering practices.
 </li>
 <li>
 Strong communication, collaboration, and mentoring skills.
 </li>
 </ul>
 
 
 
 
 
 Work in a Way That Works for You
 
 
 
 We promote a healthy work/life balance across the organisation. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance, and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.
 
 
 
 
 
 
 Working Pattern
 
 
 
 Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive
 
 
 
 
 
 
 About the Business
 
 
 
 A global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine quality information and vast data sets with analytics to support visionary science and research, health education and interactive learning, as well as exceptional healthcare and clinical practice. At Elsevier, your work contributes to the world's grand challenges and a more sustainable future. We harness innovative technologies to support science and healthcare to partner for a better world.
 
</div>

Legora

<div><h2 class="jobSectionHeader">About Us</h2>
Legora is redefining how legal work gets done. Not built for lawyers, built with them. We work alongside the world&rsquo;s best legal teams, who expect excellence, precision, and speed, and we hold ourselves to the same bar. 
 
Our AI-native workspace lets legal professionals move faster, think more clearly, and operate with sharper precision. By analysing thousands of documents in minutes and powering end-to-end workflows, we cut through complexity, teams can focus on what matters: judgment, strategy, and outcomes. 
 
1,000+ customers across 50+ countries trust us, including Cleary Gottlieb, Goodwin, Linklaters, White &amp; Case, Dentons, and Barclays. We&rsquo;ve scaled to $100M+ in ARR, with teams across Europe, North America and APAC, and continue to expand through acquisitions including Qura, Walter AI and Graceview. 
 
We partner with world-class performers: including Aaron Judge and the New York Yankees, Ludvig &Aring;berg (and his caddie), and campaigns featuring Jude Law. 
 
Joining Legora means three things.<ul><li>
We lean in: ownership over titles, outcomes over intentions.</li><li>
We fight for excellence: high standards, direct, ego-free feedback.</li><li>
We grow together: as a team and with our customers.</li></ul>
Mission before ego. Everyone contributes. No one coasts.
If you&rsquo;re driven by impact, pace, and raising the bar. This is the place.
The role 
As a Senior Data Scientist for Product at Legora you will turn data into decisions. You'll sit close to the business, taking questions end-to-end: shaping the metric, modelling the data in dbt, running the analysis, and making the recommendation. You'll pull in new data sources when you need to. Insights are useful; impact is what we hire for. 
 
We're an AI-first data team. We believe the data function should be redesigned around what AI now makes possible, not retrofitted with it, and we want someone excited to help define what that looks like in practice. 
 
There's no single profile we hire for. Some of us are strongest at data modelling and analytics engineering, some at experimentation and causal inference, some at machine learning, some at stakeholder influence. You'll likely be excellent at one or two of these and competent across the rest. That's the bar. 
 
We're a small, centralised team supporting the whole company, hiring for the person, not the seat. Depending on your strengths and where we have the biggest gap when you join, you could be embedded primarily with:<ul><li>
Product: instrumentation, feature adoption, user behaviour, A/B testing, shaping the roadmap with PMs and designers.</li><li>
Finance &amp; RevOps: ARR, NRR, forecasting, board reporting, pricing analytics across a 40-country footprint, and unit economics for an AI-native product.</li><li>
Growth &amp; Marketing: acquisition funnels, attribution, campaign measurement, lifecycle analytics, and what actually moves enterprise legal buyers.</li><li>
GTM &amp; Customer Success: pipeline analytics, customer health, expansion signals, and retention drivers in a category that didn't exist three years ago.</li></ul>
You'll partner directly with leaders across Product, Engineering, Finance, and GTM, most of whom are unusually data-fluent and will happily open a SQL editor with you. Your work will directly influence how we prioritise, how we sell, how we price, and how we build. 
 
What you will be doing<ul><li>
Partner with stakeholders across Product, Finance, GTM, Growth, and beyond to translate ambiguous questions into structured analyses and clear recommendations.</li><li>
Define the metrics that matter, design the experiments or analyses that test them, and measure the impact of what we ship.</li><li>
Conduct deep-dive analyses on the questions that move the business, and proactively surface the questions nobody is asking yet.</li><li>
Model the data you need for your work in dbt, pulling in new sources when necessary, and partner closely with data engineering on anything that needs to scale beyond your immediate use case.</li><li>
Build dashboards and reporting that scale beyond you, so the company can answer its own questions where possible.</li><li>
Help shape how the data team operates as we scale: standards, tooling, ways of working.</li></ul> 
What you'll need<ul><li>
Strong proficiency in SQL and Python.</li><li>
Solid grasp of data modelling and what it takes to build analytical work that is reliable and trusted.</li><li>
Excellent communication skills and the confidence to influence decisions through data storytelling, including pushing back when the data doesn't support what someone wants to do.</li><li>
Genuine depth in at least one of the following, with competence across the rest and curiosity to grow:<ul><li>
Data modelling and analytics engineering (dbt, dimensional modelling, semantic layers, self-service)</li><li>
Experimentation and causal inference (A/B test design, quasi-experiments, statistical rigour)</li><li>
Machine learning and applied data science (forecasting, prediction, segmentation, evaluation)</li><li>
Product analytics and metric design (funnels, cohorts, adoption frameworks, North Star metrics)</li></ul></li></ul> 
Nice to have<ul><li>
Experience in a product-led or SaaS environment, ideally B2B.</li><li>
Hands-on experience with our stack: Snowflake, dbt, Hex, Dagster.</li><li>
Prior experience as an early data hire at a fast-growing company, i.e. you've built the muscle, not inherited it.</li></ul>
Most importantly, you are someone who<ul><li>
&quot;Gets stuff done&quot; and understands building a $10bn company isn't always glamorous and takes hard work and long hours.</li><li>
Thrives in a fast-paced environment where the answers aren't always clear and processes are few.</li><li>
Is genuinely domain-curious. You don't need to have worked on every part of a business, but you should be excited to.</li></ul><div></div> 
Legora is an Equal Opportunity Employer
At Legora, we believe great teams are built on diversity of thought and experience. We&rsquo;re proud to be an equal opportunity employer and committed to creating an inclusive, high-performance culture where everyone can do their best work. We welcome people of all backgrounds and don&rsquo;t discriminate based on race, color, religion, national origin, gender, gender identity or expression, sexual orientation, age, disability, veteran status, or any other characteristic protected by law.</div>

Senior Data Scientist - Product

Oliver Bernard

<div>
 
 
 Data Scientist &ndash; Scientific Research - Hybrid, London
 
 
 
 
 
 
 Central London | Hybrid working | Up to &pound;55k + bonus
 
 
 
 
 <ul>
 <li>
 Use data and AI to accelerate scientific discovery!!
 </li>
 </ul>
 
 
 
 
 We're working with a global technology and information business helping researchers, healthcare professionals and organisations make better decisions through trusted scientific content, data and advanced analytics.
 
 
 
 
 
 We're now looking for a Data Scientist to join a growing data science organisation and work on challenging problems across machine learning, NLP, information retrieval and generative AI.
 
 
 
 
 
 What you'll be doing
 
 <ul>
 <li>
 Develop and deploy machine learning and AI solutions across search, discovery, recommendation and knowledge applications.
 </li>
 <li>
 Develop NLP and LLM-based solutions, including embeddings, semantic search, RAG and generative AI applications.
 </li>
 <li>
 Build and evaluate models using Python, SQL and modern ML frameworks such as PyTorch, TensorFlow, Scikit-learn and Hugging Face.
 </li>
 <li>
 Communicate findings clearly to technical and non-technical stakeholders and help translate ambiguous problems into practical data science solutions.
 </li>
 </ul>
 
 
 
 
 You'll ideally have:
 
 <ul>
 <li>
 Strong Python and SQL skills.
 </li>
 <li>
 Solid understanding of machine learning fundamentals, including model development, feature engineering, experimentation and evaluation.
 </li>
 <li>
 Experience working with large-scale datasets and production data science workflows.
 </li>
 <li>
 Practical experience with NLP, information retrieval, recommendation systems or generative AI.
 </li>
 <li>
 Experience with tools such as PyTorch, TensorFlow, Scikit-learn, Hugging Face, Pandas and NumPy.
 </li>
 <li>
 Experience working with APIs, data pipelines and deploying models into production.
 </li>
 <li>
 Strong statistical and analytical skills, with the ability to design robust experiments and interpret results.
 </li>
 <li>
 A degree in Data Science, Computer Science, Statistics, Mathematics or another quantitative discipline is advantageous but not essential.
 </li>
 </ul>
 
 
 
 
 Central London | Hybrid working | Up to &pound;55k + bonus
 
 
 
 
</div>

Data Scientist – Scientific Research - Hybrid, London

JPMorganChase

<div>JOB DESCRIPTION 
 
 
Payments sit at the center of the global economy, connecting businesses and consumers around the world. You will join a team driving innovation in this fast evolving space as new technologies reshape how money moves. In this role, you will work at the forefront of modern machine learning and generative AI to deliver meaningful, lasting impact on global finance. You will partner closely with product, operations, risk, and technology teams to turn ideas into measurable outcomes. You will also have opportunities to mentor others and help shape how we build and operate production AI. 
 
As a Vice President Machine Learning Data Scientist in Payments Machine Learning, you will lead the design, development, and production deployment of machine learning applications, including generative AI and agentic systems, on cloud infrastructure. You will own end-to-end delivery from problem framing and experimental design through scalable MLOps, evaluation, governance, and integration with strategic systems. You will help set technical direction, establish reusable patterns, and ensure solutions are reliable, secure, and observable in production. You will mentor engineers and data scientists and translate complex technical work into clear decisions and outcomes for stakeholders.
Job Responsibilities<ul><li>
Lead end-to-end delivery of machine learning and AI solutions for complex payments and banking operations problems, from discovery and framing to production rollout and lifecycle management</li><li>
Develop innovative machine learning solutions, including generative AI and multi-agent approaches, and define evaluation, safety, and monitoring strategies for production use</li><li>
Own production deployment patterns including containerization, continuous integration and delivery, automated testing, model and prompt registries, model and version governance, monitoring and alerting, and rollback strategies</li><li>
Architect and deploy scalable, reliable, and secure machine learning and large language model services integrated with strategic platforms and downstream consumers across APIs, batch, streaming, and event-driven patterns, meeting service level objectives</li><li>
Partner with product, operations, risk and control, and technology teams to influence roadmaps, align on requirements, and deliver data-led transformations</li><li>
Establish reusable, modular data science and machine learning capabilities that scale across use cases, including feature engineering, evaluation harnesses, prompt tooling patterns, agent frameworks, orchestration, and context and memory management</li><li>
Provide technical leadership and mentorship through code reviews, design reviews, best practices, and upskilling across data science and engineering partners</li><li>
Communicate with technical and non-technical stakeholders, translating model outputs into decisions, tradeoffs, and operational plans</li><li>
Maintain strong documentation for approaches, model cards, runbooks, and operational procedures</li></ul>
Required Qualifications, Capabilities, and Skills<ul><li>
Master's degree in a quantitative field, or equivalent practical experience</li><li>
Deep understanding of machine learning fundamentals with strong applied data analysis skills</li><li>
Demonstrated experience designing rigorous evaluation and measurement in real-world settings</li><li>
Demonstrated experience deploying and operating machine learning models in production at scale, including monitoring, drift and performance management, reliability, incident management, and continuous improvement</li><li>
Strong Python software engineering skills, including modular object-oriented design, testing, performance tuning, and debugging</li><li>
Working knowledge of MLOps and LLMOps and distributed systems, including training and serving patterns, batch versus real-time architectures, feature stores, orchestration, and scalable data processing</li><li>
Ability to design intrinsic and extrinsic evaluations aligned with business goals, including offline and online alignment and guardrails for unintended outcomes</li><li>
Experience working in regulated environments with awareness of model risk, controls, privacy and security, and audit-ready documentation</li><li>
Strong stakeholder management and teamwork skills, with the ability to drive outcomes in partnership with cross-functional teams</li></ul>
Preferred Qualifications, Capabilities, and Skills<ul><li>
Experience with NLP and generative AI, including large language models, retrieval-augmented generation, tool and function calling, agentic workflows, multi-agent orchestration, and related evaluation and safety patterns</li><li>
Familiarity with agentic building blocks and standards, including orchestration frameworks, context and memory management, and interoperability protocols such as MCP</li><li>
Experience with machine learning frameworks and data science packages such as PyTorch, TensorFlow, scikit-learn, NumPy, pandas, SciPy, and statsmodels</li><li>
Experience deploying to AWS, including services such as SageMaker and Bedrock, and operating production large language model and machine learning workloads with attention to cost, latency, performance, security, and scaling</li><li>
Experience integrating human-in-the-loop and user feedback signals into iterative improvement, including active learning, preference signals, and labeling strategies</li></ul>
ABOUT US
 
 
J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives. 
<div><div>We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. 
</div></div> 
 
ABOUT THE TEAM
 
 
J.P. Morgan's Commercial &amp; Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial &amp; Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.</div>

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