Data Scientist

• Translate complex, high-ambiguity business challenges into multi-quarter data science roadmaps
• Drive methodology, design, and standards for the centralized experimentation framework
• Serve as the primary authority on causal inference, A/B testing, and hypothesis validation
• Architect and oversee production-grade statistical and machine learning models for bid/UA optimization, dynamic audience segmentation, user lifetime value, and multi-touch attribution
• Direct time-series forecasting and real-time event processing strategies
• Partner with ML Infrastructure and Data Engineering leads on model deployment, monitoring, and integration
• Mentor senior and mid-level data scientists
• Establish code and methodology standards and foster an evidence-based culture
• Translate technical findings into strategic narratives for executive stakeholders
• Influence product strategy and commercial investments

Lead Data Scientist

• Evaluate third-party tools, platforms, and emerging technologies relevant to data migration, data infrastructure, and AI-assisted workflows
• Perform technical benchmarking, integration feasibility analysis, and build-versus-buy assessments
• Develop and apply quantitative models to assess migration risk, predict data quality issues, and validate data consistency across source and target databases
• Coordinate end-to-end customer data migration projects, including scoping, sequencing, milestone planning, risk identification, dependency management, and cross-functional execution
• Design, build, and maintain internal data pipelines and supporting infrastructure integrating structured and unstructured data sources
• Develop analytical playbooks, migration frameworks, and data-driven operational accelerators
• Implement data quality monitoring, automated regression validation, and lineage analysis workflows using Python and SQL
• Evaluate cloud and multi-cloud data environments for cost efficiency, reliability, and integration risk
• Implement data models, dashboards, and internal reporting structures
• Support technology partnerships and external cooperation efforts by assessing integration opportunities and coordinating partner-solution implementation
• Act as an internal subject matter resource for data pipeline evaluation and migration methodologies
• Translate operational findings into structured product feedback

Data Scientist – Migration Intelligence

• Develop a data science roadmap for financial systems supporting finance priorities and business objectives
• Partner with finance leadership to identify high-value opportunities, define success measures, and communicate results and recommendations
• Lead analytics, forecasting, anomaly detection, and automation initiatives across financial planning, reporting, and operational workflows
• Integrate data from relevant platforms and deploy reliable solutions into production with financial systems and engineering teams
• Establish standards for data quality, model validation, documentation, access controls, and responsible AI in finance use cases
• Coach and develop a high-performing data science team while managing priorities across multiple stakeholders
• Measure financial and operational impact and present findings to finance executives
• Stay current with industry trends and advancements in data management technologies and practices

Director, Data Science, Analytics, Finance

• Design, build, and deploy AI agents and generative AI applications from prototypes through production-ready tools
• Automate operational workflows and support commercialization and revenue-generation initiatives
• Evaluate model and AI solution performance, accuracy, and real-world impact using metrics, testing, and monitoring
• Partner with Strategy & Growth stakeholders to translate business challenges into scalable AI-enabled workflows
• Build and maintain ETL pipelines, dashboards, and reporting tools
• Conduct ad hoc data analyses
• Provide data science support for in-house digital products with potential commercial value
• Evaluate emerging AI technologies, tools, and frameworks and recommend practical applications
• Leverage modern AI development platforms, coding assistance tools, and software engineering best practices
• Report to the Director, Data Science
• Sample projects include patent operations and compliance automation, clinical-note chart review using LLMs, and RAG pipelines for strategic research insights

Data Scientist I

• Deliver data-driven insights to Firm leadership, legal practices, the Knowledge Department and administrative functions
• Support legal teams and operational staff in using data to improve decision-making and the efficiency and effectiveness of client representations
• Collaborate with Finance, Talent, Business Development, IT and other functional departments to analyze data and develop solutions
• Develop regression and classification models using established and emerging data science methodologies
• Chain, fine-tune and deploy pre-trained language models for NLP tasks including text classification, named entity recognition, summarization, clause and document generation, and question-answer exchanges
• Design and deploy document segmentation and embedding approaches for information retrieval and retrieval augmented generation
• Conduct advanced quantitative research using machine learning and natural language processing techniques
• Configure practice-specific AI workflows and language technologies, including complex pipelines, prompt engineering, prompt chaining and text operations
• Design and deploy visual reports and user interfaces that present quantitative insights to non-technical business professionals
• Monitor advancements in LLMs, NLP, deep learning and machine learning and incorporate appropriate techniques into production models
• Document development processes, codebase and best practices to support knowledge sharing and reproducibility
• Partner with technical resources to refine data pipelines for recurring analyses and data-driven solutions
• Handle projects requested by the CKIO and other executive staff

• Manage a team of three direct reports, setting deliverables, supporting career growth, and ensuring high-quality analytics output
• Analyze audience growth, traffic drivers, customer journey touch points, and user engagement trends
• Partner with Product, Design, Engineering, and Editorial leads to set strategy, define goals, and build the audience and product analytics roadmap
• Lead the design, execution, and post-analysis of product experiments and A/B tests
• Advise on primary metrics and trade-offs between competing goals
• Make recommendations to evolve product offerings
• Translate complex behavioral data and insights into clear, compelling narratives and actionable recommendations
• Support The New York Times’ value of journalistic independence and mission to seek the truth and help people understand the world

Senior Manager, Audience & Product Analytics

• Lead analytical aspects of the product lifecycle, from sizing opportunities through measuring success via experimentation for machine learning squads
• Collaborate with Ads Marketplace leads to define key analysis questions
• Conduct rigorous analysis and produce insights to improve models powering Etsy Ads
• Define metrics for robust measurement of product performance
• Partner with product managers, engineers, and applied scientists
• Guide partner teams with data and insights
• Tell the story of how Etsy attracts and retains users to teams, senior management, and the community

Senior Data Scientist, Ads Marketplace

• Participate in a 10-week paid summer internship
• Attend development workshops focused on key business concepts
• Work on meaningful projects with mentorship from managers
• Work in cross-functional teams to identify, design, and develop AI-driven solutions for real business challenges
• Engage with business stakeholders and research emerging technologies
• Build proofs of concept and iteratively refine solutions using user feedback
• Gain hands-on experience with artificial intelligence, software development, and product design
• Follow a structured innovation process culminating in presentations to senior leaders
• Support key strategic projects and complete assignments based on business needs and departmental priorities
• Perform technical and/or non-technical business-related tasks
• Create and deliver presentations at the end of the internship
• Maintain confidentiality and protect member privacy
• Demonstrate integrity and support the Lifetime Healthcare Companies' mission, values, and Code of Conduct
• Maintain regular and reliable attendance
• Perform other functions assigned by management

College Intern – AI & Data Science Cohort

• Develop NLP, LLM, and other ML-based pipelines to abstract labels from text-based healthcare data and store them in scalable data models
• Query complex healthcare source systems, including EMRs, semi-structured reports, and free-text clinical notes
• Identify key data elements and create and enrich datasets for real-world evidence analyses and AI algorithm training
• Own data extraction, wrangling, labeling, and quality-control tasks for analytical datasets
• Apply current NLP and generative AI methods to relevant use cases
• Design, test, validate, analyze, and merge multimodal data structures from varied source systems
• Develop code and documentation for high-quality, HIPAA-compliant data products
• Troubleshoot problems and ensure data accuracy, integrity, and validity
• Support the technical product team and advance data-related product offerings
• Explain abstraction methods, findings, and recommendations to technical and clinical audiences
• Report to the Data Science Manager under the Head of Data
• Attend occasional in-person company working days, roughly quarterly

NLP / LLM Data Scientist

• Design, build, and deploy AI agents and generative AI applications from prototypes through production-ready tools
• Evaluate the performance, accuracy, and real-world impact of models and AI solutions using metrics, testing, and monitoring
• Partner with Strategy & Growth stakeholders to translate business challenges into scalable AI-enabled workflows
• Perform data analytics and data engineering tasks, including building and maintaining ETL pipelines, developing dashboards and reporting tools, and conducting ad hoc data analyses
• Provide data science support for in-house digital products with potential commercial value
• Evaluate emerging AI technologies, tools, and frameworks and recommend practical applications
• Leverage modern AI development platforms, coding assistance tools, and software engineering best practices
• Build AI agents for patent operations and compliance workflows
• Develop LLM pipelines to automate chart review from unstructured clinical notes
• Build RAG pipelines extracting insights from strategic research and interviews