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

Bachelors/Masters 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)