Data Science Engineer

Job Description

Key Responsbilitiies : Develop, deploy, and maintain robust machine learning models for real-world business applications. Leverage classical ML algorithms such as regression, classification, bagging, boosting, etc., to address complex business challenges. Build and automate end-to-end ML pipelines using MLOps best practices. Manipulate and analyze large datasets using PySpark and SQL, ensuring data quality and performance optimization. Conduct statistical analyses and derive actionable insights to support decision-making. Collaborate closely with product managers, data engineers, and stakeholders to align ML solutions with business objectives. Present analytical findings clearly and concisely to both technical and non-technical audiences. Design intuitive dashboards and visualizations to track model outcomes and key metrics. Required Skills: Deep understanding of Machine Learning algorithms and Statistical Modeling techniques. Strong programming skills in Python (including pandas, NumPy, scikit-learn, etc.). Proficiency in PySpark SQL for distributed data processing. Advanced expertise in SQL for querying and manipulating structured data. Experience with MLOps frameworks and automation of ML workflows. Proven track record in predictive modeling and classical ML use cases. Strong communication and presentation skills with the ability to translate technical output into business context. Preferred Qualifications: Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP). Familiarity with data visualization libraries (e.g., Matplotlib, Seaborn, Plotly) or BI tools like Tableau/Power BI. Exposure to CI/CD practices and model monitoring tools.