Top Hybrid Data Science Jobs

Use statistical, algorithmic, and visualization techniques to model business problems, derive actionable insights from member data, and inform product and service decisions. Collaborate with end-users, build analytic capabilities, communicate data-driven recommendations, and promote advanced analytics adoption across the organization.

Lead client engagements using data science, AI, advanced analytics, statistical modeling, experimentation, and marketing measurement. Manage and develop analysts and data scientists, advise quantitative researchers, pitch projects, grow client relationships, and provide thought leadership on machine learning and emerging AI methods. Translate technical work into strategic recommendations for clients and executives.

Analyze marketing and business performance using SQL, statistics, econometric modeling, Bayesian regression, causal inference, and A/B testing. Build marketing measurement models, integrate customer and event data, develop reporting infrastructure and dashboards, evaluate marketing spend, and provide actionable recommendations to optimize growth. Partner with Marketing, Product, Engineering, Finance, and executives across multiple brands.

Develops and deploys interpretable, performant analytics and predictive modeling solutions that support business strategy, process improvement, fraud detection, portfolio optimization, and growth. Collaborates with business experts, data scientists, engineers, and IT teams to connect complex data sources, build scalable analytics tools, generate actionable insights, and deploy analytical content into business applications. Also helps business partners improve their data and analytics capabilities.

Develop machine learning and AI methods for cybersecurity products using large-scale network telemetry. Analyze anomalies and malicious behavior, engineer features, evaluate models on imbalanced and incomplete data, establish monitoring metrics, and conduct error analysis. Collaborate with threat researchers and software engineers to productionize solutions, research emerging techniques, communicate findings, document methodologies, and mentor data scientists.

Design and deploy deep learning systems for search ranking, personalized recommendations, geospatial search, and pickup-point selection. Translate business goals into ML objectives, lead offline and online evaluation, conduct A/B tests, and partner with engineering on production model serving and latency optimization. Own the production ML lifecycle, including monitoring, drift detection, retraining pipelines, and quality management across international markets.

Leads end-to-end data science and machine learning initiatives, from problem discovery and proof of concept through production deployment and continuous improvement. Develops statistical, optimization, simulation, and machine learning solutions using strong software engineering, MLOps, and Responsible AI practices. Partners with stakeholders and multidisciplinary teams, provides technical direction, evaluates feasibility and risks, communicates analytical recommendations, and remains hands-on in solution development.

Analyze digital payment adoption, engagement, transaction flows, and member behavior using large datasets. Build statistical and predictive models, dashboards, and visualizations with Python, SQL, Databricks, Power BI, or Tableau. Translate analytical findings into recommendations for product strategy, member experience, operational efficiency, economics, and risk, while presenting insights to technical and business stakeholders.

Conduct quantitative analysis on large datasets to support predictive and machine-learning model development and validation. Identify data errors, explore patterns and anomalies, answer business questions, create visualizations, and perform basic diagnostic and model suitability testing under supervision. The role also develops proficiency in statistical methods, data wrangling, analytical programming, and model-building concepts.

Analyzes business and data requirements, converts them into machine learning and generative AI problems, integrates data from multiple sources, builds data models and dashboards, performs statistical analysis, develops predictive and AI models, and benchmarks model improvements. The role requires Python expertise, knowledge of data science and visualization tools, generative AI technologies, and relational and non-relational databases.