Top Hybrid Data Science Jobs

Measure and optimize Consumer AI initiatives by developing KPI frameworks, analyzing customer and operational data, creating dashboards, supporting testing and pilots, conducting A/B experiments, and delivering executive reporting and recommendations to improve AI adoption and business value.

Leads the design, development, evaluation, and production deployment of document understanding systems for legal, tax, and accounting content. Responsibilities include semantic chunking, document enrichment, hierarchical classification, entity and citation extraction, knowledge graph construction, LLM post-training, model distillation, synthetic data generation, and evaluation framework development. The role guides architecture and AI strategy, partners with engineering on scalable delivery, influences leadership, and mentors applied scientists and ML practitioners.

Research, build, evaluate, and deploy NLP, machine learning, deep learning, and Generative AI solutions for commercial document and revenue lifecycle products. Develop LLM applications, data pipelines, embeddings, vector search, and evaluation frameworks while optimizing models for accuracy, latency, cost, and reliability. Partner with engineering, product, and data science teams to productionize solutions, monitor model performance, conduct applied research, and mentor technical colleagues.

Leads consumer marketing data science and strategy initiatives, using advanced analytics, predictive modeling, segmentation, forecasting, experimentation, and optimization to drive acquisition, engagement, conversion, and operational decisions. Partners with senior stakeholders, manages analytical projects and outsourced resources, presents executive insights, establishes analytical standards, and mentors analysts and data scientists.

Analyze behavioral data, evaluate product experiments, model user behavior, develop dashboards and forecasts, ensure tracking quality, and provide insights that influence product decisions. Collaborate with product and engineering teams while applying statistical, analytical, and AI methods to improve user experiences and business outcomes.

The Data Scientist will analyze complex healthcare datasets, build Python-based reporting pipelines, clean and validate data, and create tables, visualizations, and PowerPoint presentations. They will identify cost and utilization drivers, answer account questions, improve client-facing analytics, and collaborate with underwriting, medical management, sales, finance, and other business partners. The role requires translating complex analyses into actionable insights while managing multiple projects and deadlines.

The Senior Data Scientist will develop, validate, and operationalize AI models for diagnosing complex diseases, enhance data analysis processes, and establish MLOps infrastructure while leading data-driven initiatives.

Own product experimentation and growth analytics across signup, activation, conversion, monetization, and retention. Design and analyze A/B tests, establish statistical rigor, conduct funnel, cohort, LTV, segmentation, and retention analyses, and define trusted product metrics. Partner with Product, Engineering, Growth, and executives to translate insights into product decisions and recommendations.

Build and deploy end-to-end statistical and machine learning solutions for clients and internal delivery teams. Responsibilities include scoping ambiguous problems, analyzing operational challenges, prototyping AI workflows, translating messy data and real-world constraints into scalable solutions, iterating with stakeholders, and developing reusable tooling and infrastructure. The role combines hands-on data science with technical consulting and requires communication with technical and non-technical stakeholders.

Founding Data Scientist responsible for developing product analytics, earned media value metrics, attribution models, forecasting algorithms, and proprietary social commerce measurements. The role partners with engineering and product teams to improve data foundations, translate models into production code, and support public-facing data reports. Requires advanced Python, expert SQL, production software engineering skills, quantitative education, strong communication, and 4–7 years of data science experience.