Data Scientist-Advanced Analytics

Job Summary

We are seeking a highly motivated Data Scientist – Advanced Analytics with 3–6 years of experience in data analytics, machine learning, and AI-driven solutions. The ideal candidate should have strong expertise in Python, PySpark, SQL, Tableau, and modern Machine Learning techniques, along with hands-on experience in Large Language Models (LLMs) and Prompt Engineering. You will work with large-scale vehicle datasets, develop scalable analytics solutions, build AI-powered tools, and generate actionable insights to improve powertrain performance. Experience in the automotive domain is an added advantage.

Key Responsibilities

• Analyze large-scale structured and unstructured datasets to generate business insights.
• Develop, optimize, and maintain scalable data pipelines using PySpark, SQL, and Python.
• Perform data cleansing, validation, and transformation to build reliable analytics datasets.
• Conduct statistical analysis, trend identification, and root cause analysis.
• Design and develop interactive dashboards and visualizations using Tableau or similar BI tools.
• Collaborate with data engineers, product owners, and global stakeholders to deliver data-driven solutions.
• Build scalable analytics frameworks and reusable tools to improve operational efficiency.
• Support business teams with actionable insights for strategic decision-making.

AI & Machine Learning Responsibilities

• Develop, train, and deploy machine learning models for predictive analytics and anomaly detection.
• Apply advanced statistical techniques to identify patterns and optimize business performance.
• Design and implement AI-powered analytics solutions using Large Language Models (LLMs).
• Integrate Generative AI capabilities into analytics platforms for automated insights, summarization, and root cause analysis.
• Perform feature engineering, model evaluation, optimization, and performance monitoring.
• Explore innovative AI and GenAI use cases to improve analytics efficiency and business outcomes.

Prompt Engineering Responsibilities

• Design, optimize, and evaluate prompts for LLM-based applications.
• Build domain-specific prompts to improve response quality and model performance.
• Implement prompt chaining and context management techniques.
• Apply hallucination mitigation strategies to enhance reliability.
• Utilize AI tools such as Microsoft Copilot and other LLM platforms to improve productivity and automate analytical workflows.

Required Skills

Programming & Data Engineering

• Python (Pandas, NumPy, Scikit-learn)
• PySpark
• SQL
• Databricks
• Azure Data Platform

Data Analytics & Visualization

• Tableau or similar BI visualization tools
• Statistical Analysis
• Dashboard Development
• Data Pipeline Development
• Data Cleaning and Data Validation
• Root Cause Analysis

Machine Learning

• Regression, Classification, Clustering
• Predictive Analytics
• Feature Engineering
• Model Evaluation & Optimization
• PyTorch, TensorFlow, or MLlib

Generative AI & LLM

• Large Language Models (LLMs)
• Generative AI
• Retrieval-Augmented Generation (RAG)
• Embeddings and Context Management
• AI-powered Analytics

Prompt Engineering (Mandatory)

• Prompt Design and Optimization
• Prompt Chaining
• Prompt Evaluation
• Domain-Specific Prompt Development
• Hallucination Mitigation

Good to Have

• LangChain
• Semantic Kernel
• Azure OpenAI Services
• Vector Databases (FAISS, Pinecone)
• MLOps
• Azure AI Deployment
• Automotive or Powertrain domain experience

Educational Qualification

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Engineering, Mathematics, or any STEM discipline.

Preferred Competencies

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to work independently in a fast-paced environment.
• Collaborative mindset with experience working in global teams.
• Proactive attitude with a focus on innovation and continuous learning.