Data Scientist

• Provide technical leadership across the Analytics function
• Create, develop, and maintain AML typologies
• Collaborate with cross-functional teams and support Financial Intelligence Units (FIU)
• Oversee the execution of multiple projects
• Ensure successful project completion within established timelines

Data Scientist – FCRM

• Partner with multiple management teams to ensure appropriate integration of data science functions to meet goals.
• Identify and define necessary system enhancements to deploy new data products and process improvements, with a focus on leveraging machine learning.
• Resolve a variety of high-impact problems and projects through in-depth evaluation of complex business processes, system processes, and industry standards, applying advanced statistical analysis and machine learning modeling techniques.
• Provide subject matter expertise in data science and advanced knowledge of AI/ML applications.
• Ensure application design adheres to the overall architecture blueprint.
• Develop complex machine learning models using the Python AI/ML stack.
• Design and implement solutions for anomaly detection, noun entity recognition (NER), and rule-based compliance policies.
• Build and deploy Agentic AI solutions using Large Language Models (LLMs).
• Utilize advanced knowledge of the machine learning model lifecycle to develop and enforce standards for coding, testing, debugging, and implementation of scalable, production-grade models.
• Develop a comprehensive knowledge of how areas of business integrate to accomplish business goals and to ensure the effective deployment of data science capabilities.
• Provide in-depth analysis with interpretive thinking to define complex business issues and develop innovative, data-driven solutions and machine learning models.
• Serve as an advisor or coach to mid-level data scientists and analysts, allocating work as necessary and fostering a culture of technical excellence.
• Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients, and assets.

Senior Data Scientist – Vice President

• Design the methodology to evaluate the fitness-for-purpose of real-world data (RWD) sources for insights or evidence generation
• Lead and execute feasibility assessments for RWD sources (electronic health records, administrative claims, patient registries, wearable/digital health data) to determine suitability for specific research/business objectives
• Develop and apply structured data assessment frameworks to evaluate data quality dimensions, including accuracy, completeness, validity, timeliness, longitudinally consistency, and integrity
• Assess the availability and representativeness of patient populations within RWD sources available in Sanofi for both internal decision-making and regulatory-grade evidence generation
• Evaluate the feasibility of extracting structured and unstructured data elements (e.g., clinical scores, patient-reported outcomes) from EHR systems, including NLP-based extraction from clinical notes
• Document assessment outcomes in standardized feasibility reports and communicate findings clearly to cross-functional stakeholders
• Identify and articulate limitations of RWD sources, such as proxy endpoint constraints, population coverage gaps
• Design methodologically sound recommendations & minimize misuse of RWD, leading to unreliable insights or evidence generation
• Ensure appropriate use of ICD codes, procedure codes, and other medical coding standards (sourced from peer-reviewed references such as PubMed, Embase, and Orphanet, etc.) for patient identification, healthcare provider segmentation, clinical site identification, and phenotyping
• Apply advanced epidemiological and biostatistical methods including propensity score methods, time-to-event analyses, sensitivity analyses, and bias assessment
• Provide methodological input on the use of clinical score proxies and surrogate endpoints in RWD contexts, clearly delineating their applicability for internal versus regulatory/publication use
• Provide methodology advises ensuring deliverables from RWD Foundation, RWD Science, and RWD Products are based on medical evidence/guidelines, clinically & contextually relevant
• Work closely with analysts & data scientists to ensure methodological recommendation is realistic and implementable
• Partner with R&D, Business units (Vaccines, General Medicine and Specialty Care) & Digital teams on data identification and appropriate usage of RWD for insights / evidence generation across drug lifecycle
• Serve as the methodological point of contact for fit-for-purpose data assessment inquiries from internal stakeholders
• Collaborate with RWD Foundation, RWD Product Owners, RWD Data Sciences to ensure RWD are used appropriately to inform reliable decision making & to provide knowledge transfer on data domain expertise
• Manage external data vendors and technology partners (e.g., EHR, claims, registries) to understand data limitations and to verify methodological recommendations when required

Clinical Data Scientist – Methodologist

• Define Ads ontologies and metrics for campaigns, budgets, bids, creatives, audiences, and placements.
• Build dbt models and curated marts in Snowflake with clear data contracts, tests, and SLOs.
• Ingest and enrich unstructured Ads content and publish retrieval‑ready datasets using our managed search/vector services.
• Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and re‑ranking; set quality/latency targets and iterate via experiments.
• Design agent reasoning and policies on ads, including tool definitions and human‑in‑the‑loop approvals.
• Establish evaluation suites covering precision/recall, calibration, hallucination rate, latency, and cost.
• Run A/B or uplift experiments to quantify impact and guide iteration.
• Translate Ads problems into agent behaviors and own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time‑to‑insight.

Ads AI Analytics Lead

• We are seeking a visionary Director, R&D Neuroscience Data, Data Science & Artificial Intelligence – Ophthalmology to join the Neuroscience Data, Data Science & Artificial Intelligence (DDSAI) team.
• This leader will shape and execute innovative strategies leveraging multimodal data sources, digital health technologies, computer vision, artificial intelligence (AI), and clinical/real-world evidence (RWE) to accelerate drug discovery and development, and maximize patient impact.
• By combining ophthalmology expertise with strong data science acumen, this role will enhance clinical trial execution and ensure that new solutions are patient-centric and ready for regulatory and payer acceptance.
• As an integral member of a highly matrixed team, the Director will collaborate with cross-functional experts in the Neuroscience Therapeutic Area, Clinical Development, Quantitative Sciences, Regulatory Affairs, and Patient-Reported Outcomes, and forge strategic external partnerships to infuse new ideas and capabilities.
• This is a unique opportunity to redefine how we understand and treat eye diseases—uncovering novel digital biomarkers and endpoints, stratifying patients for more personalized care, and ultimately delivering better outcomes for people living with ophthalmic diseases.

Director, R&D Neuroscience Data, Data Science & Artificial Intelligence

• Collaborate in the development and application of advanced AI/ML methods, including cutting-edge computer vision techniques applied to ophthalmic imaging data (e.g., Optical Coherence Tomography and fundus images), to uncover disease mechanisms and identify novel biomarkers.
• Collaborate in the development and validation of novel digital endpoints.
• Engage with regulatory stakeholders to ensure these innovations enhance clinical trial design, improve patient monitoring and care pathways, and meet regulatory requirements.
• Develop and apply sophisticated statistical models using real-world and clinical data to generate insights into disease progression, treatment outcomes, and patient stratification.
• Leverage longitudinal disease modeling, Bayesian methodologies, and causal inference techniques to inform decision-making.
• Apply emerging generative AI approaches to boost data analysis and knowledge discovery, integrating diverse multimodal datasets (imaging, clinical, wearable, etc.) for a more holistic understanding of ophthalmic diseases.
• Partner with Clinical Development and Medical Affairs to integrate RWE into evidence generation strategies.
• Support trial optimization and regulatory submissions by incorporating insights from large-scale clinical datasets, electronic health records (EHRs), and other real-world data sources.
• Build strong cross-functional collaborations within the company and spearhead external partnerships with academic institutions, technology providers, regulators, and industry consortia.

Associate Director, R&D Neuroscience Data, Data Science & AI – Ophthalmology

• Drive enterprise aligned AI governance across Data Science & Digital Health (DSDH) and the broader R&D organization
• Implement high quality, compliant, and scalable governance practices for both traditional AI/ML and GenAI systems
• Partner closely with DSDH teams, Quality, Legal, Global Regulatory Affairs, and Johnson & Johnson Technology to streamline governance processes
• Embed AI safety protocols and align with enterprise strategic priorities
• Develop and maintain an AI governance roadmap that supports R&D strategy
• Develop, implement, and drive adoption of quality assurance processes for AI/ML and GenAI development
• Integrate those processes with existing medical product and digital health tool development processes
• Create and disseminate playbooks, toolkits, and standardized guidance to support teams in implementing responsible AI practices
• Establish and track metrics to evaluate governance effectiveness including processes for audits, reviews, and risk based assessments of AI systems
• Serve as a technical and strategic advisor on AI governance topics
• Represent Johnson & Johnson in external AI governance forums and consortia

Associate Director – AI Quality and Governance, R&D Data Science & Digital Health

• Drive the development and application of advanced AI/ML methods, including cutting-edge computer vision techniques applied to ophthalmic imaging data (e.g., Optical Coherence Tomography and fundus images).
• Lead the development and validation of novel digital endpoints.
• Engage with regulatory stakeholders to ensure these innovations enhance clinical trial design, improve patient monitoring and care pathways, and meet regulatory requirements.
• Develop and apply sophisticated statistical models using real-world and clinical data to generate insights into disease progression, treatment outcomes, and patient stratification.
• Leverage longitudinal disease modeling, Bayesian methodologies, and causal inference techniques to inform decision-making.
• Apply emerging generative AI approaches to boost data analysis and knowledge discovery, integrating diverse multimodal datasets (imaging, clinical, wearable, etc.) for a more holistic understanding of ophthalmic diseases.
• Partner with Clinical Development and Medical Affairs to integrate RWE into evidence generation strategies.
• Build strong cross-functional collaborations within the company and spearhead external partnerships with academic institutions, technology providers, regulators, and industry consortia.

Director, R&D Neuroscience Data, Data Science & Artificial Intelligence – Ophthalmology

• Develop and maintain an AI governance roadmap that supports R&D strategy, enabling informed decision making and proactive management of AI related risks.
• Develop, implement, and drive adoption of quality assurance processes for AI/ML and GenAI development, integrate those processes with existing medical product and digital health tool development processes, and ensure adherence to internal and external regulatory standards.
• Drive alignment on AI quality/governance initiatives across R&D and enterprise, partnering with JJT to streamline governance workflows and promote consistent adoption across R&D.
• Create and disseminate playbooks, toolkits, and standardized guidance to support teams in implementing responsible AI practices.
• Establish and track metrics to evaluate governance effectiveness, including processes for audits, reviews, and risk based assessments of AI systems.
• Serve as a technical and strategic advisor on AI governance topics
• Collaborate with therapeutic areas, functional partners, and senior stakeholders to drive cross functional collaboration and alignment.
• Represent Johnson & Johnson in external AI governance forums and consortia, contributing to industry wide standards and best practices.

Associate Director, AI Quality and Governance – R&D Data Science, Digital Health

• provides technical leadership across the overall Analytics function which may have an enterprise mandate
• provides deep technical knowledge and expertise in client interactions to explain complex data analysis related material
• develops, maintains, and enhances the Enterprise Anti-Money Laundering / Counter-Terrorism Financing (AML/CTF) models/AI solutions to comply with regulatory requirements/changes and internal policies
• supports TD's global AML/CTF strategies, address emerging risks, and be in accordance with best industry practice
• seeks talented data scientists to join to innovate, drive, and support initiatives and business as usual operations in multiple functional areas

Data Scientist III – Financial Crime Risk Modeling

• Develop, analyze, evaluate, and deliver actionable analytics that inform key supply chain decisions, optimize performance, and support the successful execution of strategic initiatives
• Lead the development, optimization, and continuous improvement of databases, data pipelines, and analytics infrastructure to ensure data integrity, accessibility, and performance
• Own the design of Power BI dashboard to support the business needs and the ability to build tabular and/or visualization reports as needed
• Develop, implement, and maintain databases, data collection systems, data analytics and other strategies that optimize statistical efficiency and quality with data from primary or secondary data sources.
• Lead the identification, analysis, and interpretation of trends or patterns in complex data sets using statistical techniques
• Perform extensive data validation/quality assurance analysis within large datasets
• Share compelling, insightful storytelling so the business results/needs are understood

Supply Chain Analytics Lead

• Lead a team of business data analysts responsible for implementation, enhancement, and maintenance of AML Transaction Monitoring platform
• Bring strong domain expertise in Securities Compliance and understand transaction patterns in trading
• Collaborate with Technology, Data, and Modeling teams for end-to-end delivery
• Ensure engagement with Audit and Compliance stakeholders to support issue validation

AML Business Data Manager

• Develop and deploy large-scale ML and GenAI-powered products and pipelines.
• Own all stages of the data science project lifecycle, including: Develop, deploy, monitor, and scale models through the full Software Development Life Cycle into production (including both ML and GenAI services).
• Perform exploratory data analysis, proof-of-concepts, model benchmarking, and validation experiments for both ML and GenAI approaches.
• Partnering with business leaders, domain experts, and end-users to gather requirements and align on success metrics.
• Follow coding standards, perform code reviews, and optimize data science workflows.
• Evaluation, interpretation, and communication of results to executive stakeholders.

Senior Data Scientist

• Collect data and use data science techniques to uncover insights
• Provide oversight and consultative advice as the lead Data Scientist
• Evaluate technical solutions and lead innovation to solve complex business issues
• Provide strategic and day-to-day direction on CTR program activities
• Coordinate with internal and external stakeholders for audits and examinations

Lead Data Scientist

• Serve as the primary point of contact for At Work-related data and analytics needs
• Partner with CAPTRUST’s centralized data team to prioritize, design, and improve data solutions supporting At Work
• Help establish consistent definitions, metrics, and reporting standards across the At Work practice
• Improve data quality, clarity, and usability over time
• Support and enhance internal and client-facing reporting used by At Work teams
• Work closely with Operations to understand recurring reporting needs and reduce one-off or manual efforts
• Develop dashboards and reports that support operational planning, advisor effectiveness, and leadership decision-making
• Translate reporting outputs into insights that inform action, not just describe activity
• Develop qualitative and quantitative measures of client and participant engagement, trust, and relationship health
• Help establish ongoing feedback loops across the client and participant lifecycle, beyond episodic surveys
• Identify trends that signal strengthening relationships, stagnation, or potential breakdowns
• Support analysis related to the annual financial wellness survey and other experience-related initiatives
• Analyze patterns associated with strong long-term outcomes such as sustained engagement, retention, and expansion of advisory relationships as needs evolve
• Partner with leadership and Operations to identify where additional investment of time or resources may be needed
• Help reduce reliance on anecdotal feedback through clearer, shared insight

Data & Analytics Lead

• **Key Responsibilities**
• - **Build from Scratch:** Establish, scale, and own the data analytics function, infrastructure, and metrics frameworks across the entire organization.
• - **Cross-Stack Data Mapping:** Integrate disparate data sources across onchain activities, product usage, financial transactions, and operations into a cohesive, centralized data model.
• - **Drive Business Insights:** Deepen customer understanding by transforming raw behavioral data into strategic insights that influence product roadmaps and business growth.
• - **Enable Self-Service Analytics:** Design, deploy, and maintain intuitive dashboards and reporting structures (e.g., Metabase, Looker) to empower cross-functional teams to make rapid, independent, data-driven decisions.
• - **Engineering Partnership:** Collaborating closely with the engineering team to optimize data pipelines, implement product analytics tracking, and elevate the capabilities of the data stack.
• - **Executive Communication:** Synthesize complex data findings into clear, high-impact recommendations for the executive leadership team.

Founding Data Lead

• Setting the technical direction and measurement strategy for the shopping funnel
• Developing and mentoring data scientists across experimentation, causal inference, and product analytics
• Defining scalable frameworks for A/B testing, funnel optimization, and consumer behavior modeling
• Influencing senior product and business stakeholders with data-driven recommendations
• Ensuring analytical rigor translates into tangible product improvements and business growth
• Driving deep-dive analyses of renter behavior, market dynamics, and funnel performance to identify high-impact growth opportunities
• Managing and developing a team of data scientists working across shopping funnel measurement and consumer analytics
• Raising the bar on production-grade analytics and experimentation
• Operating as a strategic thought partner to senior stakeholders across Product, Engineering, and Business Strategy
• Leading thoughtful adoption of AI within your team.

Manager, Data Science

• drive profitable growth and business innovation through machine learning techniques
• spearhead high-impact data science projects to improve product personalization and customer experience
• work closely with data scientists, ML engineers, software developers, and business stakeholders
• architect agentic AI solutions to optimize offer personalization and automate customer queries
• develop predictive models to forecast customer behavior and business events
• oversee the end-to-end development and operational lifecycle
• lead discussions with key business leaders to align data science initiatives with business strategies
• partner with engineering teams to deploy models into production
• serve as a subject matter expert on agentic development and machine learning
• drive technical excellence through active research

Principal Data Scientist

• Shape and execute innovative strategies leveraging multimodal data sources, digital health technologies, computer vision, artificial intelligence (AI), and clinical/real-world evidence (RWE) to accelerate drug discovery and development and maximize patient impact.
• Collaborate with cross-functional experts in the Neuroscience Therapeutic Area, Clinical Development, Quantitative Sciences, Regulatory Affairs, and Patient-Reported Outcomes.
• Build strong cross-functional collaborations within the company and spearhead external partnerships with academic institutions, technology providers, regulators, and industry consortia.

• Frame ambiguous business and scientific questions into measurable AI product hypotheses, success metrics, evaluation plans, and rapid experiments.
• Contribute to six-sprint, 12-week AI Accelerator agile cycles by testing hypotheses, validating AI product increments, and adapting analyses during two-week sprints.
• Build data science prototypes using Python, SQL, notebooks, APIs, and AWS-aligned data services.
• Support sandboxed data problem solving in non-production environments, enabling agents and analysts to branch, transform, test, and audit code-plus-data experiments before promotion.
• Evaluate and curate the analytical context agents and analysts rely on, including explicit instructions, memory, data tools, and curated meaning from source materials and recommend improvements based on measured impact on agent quality.
• Develop analytical features, embeddings, classifiers, ranking/scoring methods, recommendation logic, simulation approaches, or optimization methods as needed for product outcomes.
• Partner with Data Engineers to shape reliable datasets, retrieval corpora, metadata, and feature pipelines using S3, Athena, PostgreSQL/RDS, vector databases, and knowledge graphs.
• Design and execute evaluations for LLM, RAG, and agentic workflows, with emphasis on context quality, knowledge curation, semantic evolution, and model quality.
• Build evaluation rubrics, golden datasets, structured output validation, error taxonomies, hallucination risk measurement, and SME review loops.
• Use tools such as LangGraph, LangSmith, PydanticAI, or similar frameworks to test agent behavior, retrieval quality, reasoning traces, and workflow reliability.
• Evaluate whether curated enterprise context improves agent quality, reliability, traceability, and decision usefulness compared with raw document retrieval.
• Assess model and agent outputs for quality, uncertainty, calibration, bias, hallucination risk, traceability, and fitness for intended use.
• Explore approved proprietary and open model options through enterprise channels and recommend model/task pairings based on evidence, risk, cost, and performance.
• Define KPIs and analytical measurement plans for AI products, including adoption, user behavior, workflow efficiency, scientific utility, and business value.
• Use bi-weekly demos, sprint reviews, stakeholder feedback, and performance results to measure MVP progress and assess readiness for scaling or production transition.
• Apply statistical modeling, experimental design, causal inference, or quasi-experimental methods where appropriate to separate signal from noise.
• Create clear analyses, visualizations, and narratives that help product teams and stakeholders understand model behavior, limitations, and opportunities.
• Partner with responsible AI, security, quality, and domain experts to ensure evaluations and analytics respect data privacy, scientific integrity, and enterprise governance.
• Contribute reusable notebooks, context-quality evaluation harnesses, analytics templates, prompt/evaluation assets, and data science patterns that can be adopted across pods.
• Participate in code reviews, analysis reviews, design discussions, and technical problem-solving with engineering and product teams.
• Use coding agents and AI-assisted development tools effectively while validating outputs, documenting assumptions, and maintaining scientific rigor.
• Continuously refine analytical priorities and backlogs as insights emerge, incorporating stakeholder input, performance results, and lessons learned throughout MVP development.
• Coach peers on practical data science, evaluation design, measurement strategy, and evidence-based decision making in fast-moving AI delivery environments.

See all jobs on JobTailor

Data Scientist – FCRM

Senior Data Scientist – Vice President

Clinical Data Scientist – Methodologist

Ads AI Analytics Lead

Director, R&D Neuroscience Data, Data Science & Artificial Intelligence

Associate Director, R&D Neuroscience Data, Data Science & AI – Ophthalmology

Associate Director – AI Quality and Governance, R&D Data Science & Digital Health

Director, R&D Neuroscience Data, Data Science & Artificial Intelligence – Ophthalmology

Associate Director, AI Quality and Governance – R&D Data Science, Digital Health

Data Scientist III – Financial Crime Risk Modeling

Supply Chain Analytics Lead

AML Business Data Manager

Senior Data Scientist

Lead Data Scientist

Data & Analytics Lead

Founding Data Lead

Manager, Data Science

Principal Data Scientist

Associate Director, R&D Neuroscience Data, Data Science & AI – Ophthalmology

Senior Data Scientist