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) .