Data Science Lead
3 days ago
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 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 :
- 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
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