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No Recruiters Please.Must be in office 5 days / week.Minimum 3+ years of experience.Must come onsite for interview.We are seeking a Data Scientist who brings a unique blend of data science and data engineering expertise. The ideal candidate will have hands-on experience with machine learning algorithms, extensive work in building data pipelines, and proven experience with time series data. This role requires a skilled professional who can not only develop data-driven models but also set up MLOps infrastructure to support scalable deployment and continuous integration of machine learning solutions.
ResponsibilitiesDevelop recommendation system and risk models.Develop data pipelines to ingest, clean data, model and extract insights from data.Generate actionable insights from data for decision makers.Develop and implement risk models to assess loss probabilities, risk metrics, and other financial impacts.QualificationsB.S/M.S. in Machine Learning, Statistics, Computer Science, Mathematics, or other quantitative fields.3+ years of experience in data science, data engineering, and actuarial modeling.Proficiency in machine learning algorithms, with a strong focus on time series analysis.Experience building and maintaining data pipelines using tools such as Apache Spark, Airflow, and SQL.Fluency in a programming language (Python, SQL) and machine learning tools.MLOps: Knowledge of MLOps tools and practices, with experience setting up CI/CD pipelines for machine learning models (e.g., Docker, Kubernetes, TensorFlow Serving).Experience with cloud platforms such as AWS, Azure, or Google Cloud for data storage, machine learning model deployment, and data processing.Familiarity with visualization tools (Tableau/QuickSight/Power BI).
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