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)