Data Scientist – Data Operations R&D

Rwindia; Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Rwindia in Bengaluru invites a Data Scientist – Data Operations R&D to design, develop, and deploy advanced AI/ML and analytics solutions for automotive and manufacturing use cases such as predictive maintenance and supply chain optimization.

You will build end-to-end data science pipelines, experiment with Generative and Agentic AI, deploy models on cloud platforms, create dashboards, and collaborate with data engineering, product, and domain teams to translate complex analytics into business

Qualifications

• 4–5 years of experience in Data Science, ML, or AI development.
• Strong statistics and probability skills including hypothesis testing and Bayesian methods.
• Experience with ML algorithms (supervised/unsupervised, ensembles) and model tuning.
• Hands-on AI/Deep Learning experience (NLP, CV, DNNs) and Generative/Agentic AI.
• Proficiency in Python (Pandas, NumPy, scikit-learn, TensorFlow, PyTorch) and cloud platforms (Azure/AWS/GCP).
• Experience with data platforms Snowflake, Databricks, Spark, and SQL; deployment via APIs, Docker, MLflow, CI/CD.

Responsibilities

• Develop, validate, and deploy ML/AI models to solve business challenges.
• Design end-to-end data science pipelines: ingestion, feature engineering, training, evaluation, deployment.
• Build and operationalize Agentic AI systems with autonomous agents and multi-agent workflows.
• Develop predictive analytics including time-series forecasting and anomaly detection.
• Integrate data from IoT, telematics, MES, ERP, and connected vehicle platforms.
• Deploy scalable AI/ML models on Azure, AWS, or GCP.
• Create dashboards and visualizations to communicate findings.
• Collaborate with data engineering, product, business stakeholders, and domain experts.
• Stay updated with emerging AI/GenAI technologies.
• Work on automotive and manufacturing analytics use cases like predictive maintenance and supply chain optimization.

Skills

Statistics

Probability

Machine Learning

Deep Learning

NLP

Computer Vision

Generative AI

Agentic AI

LLMs

Prompt Engineering

RAG

Python

Pandas

NumPy

Scikit-learn

TensorFlow

PyTorch

Snowflake

Databricks

Spark

SQL

APIs

Docker

MLflow

CI/CD

Azure

AWS

GCP

Automotive analytics

Manufacturing analytics

Communication

Problem solving

Tools

Docker

MLflow

CI/CD pipelines

Job description

Job Summary

The Data Scientist – Data Operations R&D is responsible for designing, developing, and deploying advanced AI/ML and analytics solutions in enterprise environments. The role focuses on machine learning, statistical modeling, predictive analytics, Generative AI, Agentic AI systems, cloud deployment, and solving complex automotive and manufacturing use cases such as predictive maintenance, supply chain optimization, and connected vehicle analytics.

Key Responsibilities

• Develop, validate, and deploy Machine Learning and AI models to solve business challenges.
• Apply advanced statistical techniques including hypothesis testing, regression analysis, Bayesian methods, and A/B testing for data-driven insights.
• Design and implement end-to-end data science pipelines including data ingestion, feature engineering, model training, evaluation, and deployment.
• Build and operationalize Agentic AI systems including autonomous agents, multi-agent workflows, and LLM-based reasoning systems.
• Develop predictive analytics solutions including time-series forecasting, anomaly detection, and business intelligence models.
• Work on automotive and manufacturing use cases such as predictive maintenance, quality analytics, defect detection, supply chain optimization, and production planning.
• Integrate data from IoT, telematics, MES, ERP, and connected vehicle platforms for advanced analytics solutions.
• Deploy scalable AI/ML models using cloud platforms such as Azure, AWS, or GCP.
• Build APIs and containerized deployments using Docker and modern deployment frameworks.
• Create dashboards, visualizations, and presentations to communicate analytical findings.
• Collaborate with cross-functional teams including data engineering, product, business stakeholders, and domain experts.
• Stay updated with emerging technologies in AI/ML, GenAI, and Agentic AI ecosystems.

Required Skills & Experience

• 4–5 years of experience in Data Science, Machine Learning, or AI development roles.
• Strong expertise in Statistics and Probability including Hypothesis Testing, Regression Models, Bayesian Methods, and A/B Testing.
• Strong experience in Machine Learning algorithms including supervised and unsupervised learning, model tuning, and ensemble techniques.
• Hands‑on experience in AI and Deep Learning including NLP, Computer Vision, and Deep Neural Networks.
• Experience with Generative AI and Agentic AI frameworks.
• Strong experience with LLMs (GPT, Llama, etc.), Prompt Engineering, RAG, and autonomous agents.
• Advanced Python programming skills.
• Hands‑on experience with Python libraries including Pandas, NumPy, Scikit‑learn, TensorFlow, and PyTorch.
• Experience with Data Platforms including Snowflake, Databricks, Spark, and SQL.
• Experience with Model Deployment including APIs, Docker, MLflow, and CI/CD pipelines.
• Familiarity with Cloud Platforms such as Azure (preferred), AWS, or GCP.
• Experience in automotive or manufacturing analytics environments.
• Strong analytical and problem‑solving skills.
• Ability to communicate complex technical models to non‑technical stakeholders.

Preferred Qualifications

• Experience working with streaming data technologies such as Kafka and Spark Streaming.
• Knowledge of Digital Twins and Industry 4.0 concepts.
• Exposure to MLOps frameworks and deployment automation.
• Experience with graph‑based AI or multi‑agent systems.
• Understanding of data governance and model explainability frameworks.

Other Requirements

• Strong communication and storytelling capabilities.
• Team‑driven mindset with strong stakeholder management skills.
• Ability to solve complex real‑world business problems using advanced analytics.