Data Science Lead - R/Python

Key Responsibilities :

Strategic Problem Solving :

- Understand business objectives and translate them into data science problems.

- Provide expert-level solutions with a strong focus on forecasting methodologies.

Pipeline and Model Development :

- Design, develop, and maintain robust data processing pipelines.

- Create reproducible modeling pipelines for scalability and efficiency.

Data Analysis and Insights :

- Explore and analyze data to extract actionable insights.

- Clearly communicate findings to stakeholders, providing data-driven recommendations.

Team Leadership :

Manage and mentor a team of Data Scientists and Senior Data Scientists.

- Foster a collaborative environment that encourages innovation and growth.

Model Testing and Iteration:

- Analyze experimental results and refine models to maximize business impact.

- Experiment with new techniques and algorithms to stay at the forefront of data science.

System Integration :

- Build and integrate predictive models into analytic systems.

- Test the performance of data-driven solutions in real-world scenarios.

Qualifications and Skills :

Essential Requirements

- Experience : 4-6 years in Data Analytics, model development, and deployment.

- Forecasting Expertise : Proven ability to apply forecasting techniques effectively.

- Technical Proficiency : Hands-on experience with SQL, R, Python, and Hadoop.

- Skilled in developing predictive analytics and machine learning algorithms.

- Familiarity with libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, and Pandas.

- Experience with cloud platforms such as AWS, Azure, or GCP for data science workflows.

- Data Management : Strong knowledge of data visualization and management techniques using tools like Tableau, Power BI, or Matplotlib.

- Leadership : Experience managing teams of 2-3 or more members.

- Strong organizational and team leadership skills.

- Educational Background : Bachelors degree (B. Tech/BE) in Computer Science, Data Science, Mathematics, or a related field.

Preferred Qualifications :

- Understanding of business applications and the integration of analytics into business operations.

- Familiarity with deploying analytic solutions at scale.

- Exposure to Natural Language Processing (NLP) techniques and tools.

- Knowledge of Big Data technologies like Spark or Kafka.

- Certification in Data Science or related fields, such as Certified Analytics Professional (CAP) or Google Data Engineer