Assistant Manager - Data Scientist

Assistant Manager – Data Scientist

We are looking for an experienced Data Scientist to join our team and drive data-driven decision-making across the organization. The ideal candidate will have a strong foundation in statistical analysis, machine learning, and business problem-solving, with proven experience translating data into actionable insights.

Key Responsibilities

• Design, build, and deploy machine learning models to solve business problems (classification, regression, clustering, recommendation systems, etc.)
• Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies in large datasets
• Collaborate with product, engineering, and business teams to define data science use cases and success metrics
• Develop and maintain data pipelines in partnership with data engineering teams
• Conduct A/B testing and statistical experiments to validate hypotheses and measure impact
• Communicate findings and recommendations to both technical and non-technical stakeholders through reports, dashboards, and presentations
• Own end-to-end model lifecycle: from data collection and feature engineering to model deployment and monitoring
• Stay current with the latest research and best practices in data science and machine learning
• Mentor junior data scientists/analysts as needed

Required Skills & Qualifications

• Bachelor's/Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related field
• 8+ years of hands-on experience in data science, applied machine learning, or a similar analytical role
• Strong proficiency in Python (Pandas, NumPy, Scikit-learn) and/or R
• Solid understanding of statistics, probability, and experimental design
• Experience with SQL and working with relational/non-relational databases
• Hands-on experience with ML frameworks such as Scikit-learn, XGBoost, TensorFlow, or PyTorch
• Experience with data visualization tools (Tableau, Power BI, or Matplotlib/Seaborn)
• Familiarity with cloud platforms (AWS, GCP, or Azure) for model deployment
• Strong problem-solving skills and ability to work with ambiguous business problems
• Excellent communication skills to present technical findings to non-technical audiences
• Experience with MLOps tools (MLflow, Airflow, Docker, Kubernetes)
• Exposure to NLP, computer vision, or time-series forecasting
• Experience working in Agile/Scrum environments
• Knowledge of big data tools (Spark, Hadoop)