Note :
Be one of the first applicants, read the complete overview of the role below, then send your application for consideration.
- 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.
Responsibilities
- Develop 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.
Qualifications
- B.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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