Data Scientist

1 month ago

Bengaluru Karnataka, India SKF Full time

Requisition ID: 21089- Job Category: Information Technology- Career level: Professional- Contract type: Permanent- Location: Bengaluru, IN**Dear Colleagues,**

**JOB** **DESCRIPTION**:
**Job Title**: Data Scientist

**Reports To**: Product & Data Engineering Manager, Vehicle Aftermarket

**Role Type**: Individual Contribution

**Location**: Bangalore

**Role** **Purpose**:
The Vehicle Aftermarket team leverages data analytics to drive efficiencies and insights in the automotive aftermarket. As a Data Scientist, you will oversee projects from data collection and model development to deployment, providing actionable insights that drive strategic business decisions for Vehicle Aftermarket division.

**Key** **Responsibilities**:

- **Pipeline Management & ETL**: Partner with Data Engineering to design and manage data pipelines on Snowflake and Azure, using ETL/ELT tools (e.g., DBT, Alteryx, Talend, Informatica).
- **Machine Learning & AI**: Deploy advanced AI/ML tools (TensorFlow, PyTorch, AutoML, NLP, Computer Vision) and use predictive modeling to enhance customer experience, revenue, and business outcomes.
- **Performance Monitoring**: Implement tools to monitor and analyze model performance and data accuracy.
- **Cross-functional Collaboration**: Work with teams across the organization to identify and execute data-driven business solutions.
- Build, enhance, and maintain predictive models and analytic strategies focused on customer behavior
- Lead and modeling projects, operational tests (e.g., A/B and multivariate), and data mining and exploratory work to produce actionable and novel insights and recommendations
- Partner with stakeholder groups to understand market needs and drive data initiatives beyond our current product portfolio
- Engage in independent research projects to uncover and understand new tools, methods, and approaches that can strengthen the data science platform
- Create new data assets to support data science project work

**Metrics**
- **Technical metrics**: Model accuracy, data quality, feature engineering effectiveness, scalability, efficiency, code quality, deployment time, maintenance, reproducibility, and experimentation.
- **Business metrics**: Return on Investment (ROI), Customer Satisfaction, Revenue Growth, Churn Rate, Business Insights, Adoption Rate, User Engagement

**Competencies**
- **Educational Background**: Master’s in data science, Statistics, Computer Science, or related field.
- **Programming & Analytics**: Proficiency in Python (including Pandas, NumPy, scikit-learn) or R for data analysis and machine learning tasks.
- **Machine Learning & AI**: Hands-on experience with ML frameworks such as TensorFlow and PyTorch. Familiarity with AutoML tools, NLP libraries (e.g., spaCy, NLTK), and Computer Vision technologies.
- **Data Management & Architecture**: Strong SQL skills for data extraction and manipulation. Proficiency with Snowflake for data warehousing and familiarity with Azure infrastructure, including services like Azure Data Factory, Azure Synapse, and Azure Machine Learning.
- **ETL/ELT & Orchestration Tools**: Exposure to ETL/ELT tools like DBT, Alteryx, Talend, or Informatica.
- **Cloud Computing**: Exposure to Azure (preferred), AWS (S3, Redshift, Aurora, SageMaker), or Google Cloud Platform for cloud storage, processing, and machine learning services.
- **Database Management**: Solid understanding of database management systems and data architecture principles to maintain data quality, accuracy, and scalability.
- **Version Control & Agile Development**: Experience with Git, DevOps, and Agile methodologies.
- **Soft Skills**: Strong problem-solving, teamwork, adaptability, and communication skills, with the ability to present complex findings to non-technical stakeholders.
- **Additional Knowledge**: Exposure to data governance, security, and ERP systems (e.g., XA, SAP).
- M.E/MTech (preferred) B.E/B. Tech: CSE, IT or any relevant
- 6 - 10 Years ( Minimum 3 Years of Experience in Data Science)

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