Top Hybrid Data Science Jobs

Data Science Interns will learn to extract, transform, cleanse, and analyze data from multiple sources; evaluate machine learning results; measure marketing effectiveness; forecast activities; and produce analytical reports. The role emphasizes research, quantitative analysis, communication, project prioritization, and collaboration with mentors and senior leaders.

Leads advanced data science initiatives focused on resiliency risk identification, scenario analysis, service stability, and performance insights. Develops statistical and machine learning models, conducts complex analytical experiments, and translates findings into actionable recommendations for senior leadership. Consults on data science methods, communicates insights to nontechnical audiences, mentors peers, and collaborates across functions to resolve complex business challenges and drive strategic initiatives.

Develop and validate machine learning models, quantify model risks, challenge production models, and contribute to enterprise model governance. Analyze large-scale numeric and textual datasets using Python, AWS, Spark, and related tools. Partner with data scientists, engineers, and product managers, validate models across business domains, and communicate model-risk implications to executives.

Develops AI and machine learning prototypes for insurance applications using Python, large language models, NLP, retrieval-augmented generation, agentic AI, information retrieval, and computer vision. Collaborates with data scientists and engineers on ideation, product design, algorithm development, data analysis, and experimentation in an R&D environment.

Develops and deploys scalable data science, machine learning, Generative AI, and advanced analytics solutions. Responsibilities include SQL development, data preparation, feature engineering, predictive modeling, model evaluation, prompt engineering, embeddings, vector databases, RAG applications, monitoring, and responsible AI practices. The role collaborates with business and technology teams to deliver production-ready solutions and communicate actionable insights.

Lead high-impact consumer data science initiatives across experimentation, causal inference, analytics, search, personalization, and product measurement. Set technical standards and multi-quarter analytics roadmaps, mentor data scientists, develop AI-native workflows and self-service tools, and communicate recommendations to product, engineering, and executive stakeholders. The role requires independent ownership of ambiguous analytical problems and technical leadership without formal people management.

Design and implement data science, machine learning, generative AI, and process automation solutions for regulatory operations. Develop analytical models, LLM applications, dashboards, ETL workflows, and automated processes using Python, SQL, SAS, Alteryx, Tableau, AWS, and Snowflake. Establish data quality, model governance, KPI, and technical standards; translate findings into executive insights; and collaborate with stakeholders to improve efficiency, compliance, and financial outcomes.

Build AI- and automation-driven analytics workflows for SEO and AEO marketing performance. Responsibilities include automating reporting, anomaly detection, root-cause analysis, data exploration, segmentation, visualizations, and insight generation. The role partners with analysts and stakeholders to translate business questions into reusable frameworks, refine prompts and agent logic, and develop scalable analytical tools using Python, SQL, GenAI, and marketing analytics platforms.

Develop and optimize generative AI and large language model solutions, including fine-tuning, agentic workflows, retrieval-augmented generation, and experimentation. Research emerging algorithms, evaluate model performance using automated and human-in-the-loop methods, implement safety guardrails, and integrate AI services with cloud platforms. Mentor junior data scientists and guide technical approaches for AI-driven products.

Lead AI, machine learning, and data science workstreams from methodology through production delivery. Develop scalable, maintainable analytical code, explore new methods, influence product decisions, and collaborate with engineering, product, business, and data teams. Translate technical tradeoffs into actionable recommendations, present findings to technical and business audiences, and deliver measurable business impact while managing risks and maintaining methodological quality.