Husk Power Systems - Data Scientist - Python/R (4-6 yrs)

About the Role :

We are looking for a highly motivated and analytical Data Scientist to join our team and help us extract actionable insights from large volumes of data.

You will work closely with business stakeholders, data engineers, and software developers to create data-driven solutions that solve real-world problems and drive strategic decisions.

This is an exciting opportunity for a data enthusiast who thrives in a dynamic, results-driven environment and is eager to apply machine learning, statistical modeling, and data mining techniques in a production setting.

Key Responsibilities :

- Analyze structured and unstructured data to uncover trends, patterns, and insights.

- Build predictive models and machine learning algorithms to solve business problems (e.g., classification, regression, clustering, recommendation).

- Develop and validate statistical models and conduct hypothesis testing.

- Design and implement end-to-end data science workflows, from data ingestion and wrangling to model deployment and monitoring.

- Visualize data using tools like Matplotlib, Seaborn, Plotly, or Power BI/Tableau to communicate insights effectively.

- Collaborate with product managers, engineers, and analysts to integrate models into business processes or applications.

- Continuously monitor and optimize model performance in production environments.

Required Skills & Qualifications :

- Bachelors or Masters degree in Computer Science, Statistics, Mathematics, Data Science, or related fields.

- 26 years of hands-on experience as a Data Scientist or similar role.

- Proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, etc.) or R.

- Strong SQL skills for data extraction, transformation, and analysis.

- Experience with machine learning models and statistical techniques (regression, decision trees, clustering, etc.

- Understanding of data preprocessing, feature engineering, and model evaluation techniques.

- Experience working with large datasets and distributed computing tools like Spark, Dask, or Hadoop (nice to have).

- Familiarity with version control systems (e.g., Git) and collaborative tools (e.g., Jupyter, VS Code, or Google Colab)