GEN AI Architect

Johnson Electric; Chennai District; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

Johnson Electric in Chennai seeks a data scientist to translate business problems into machine learning solutions. You will design experiments, build production-grade models, and collaborate with product and engineering teams.

You will own end-to-end data projects, perform SQL analysis, ensure model reliability, and communicate insights to stakeholders, shaping data-driven decisions across the business. A strong foundation in Python/R and statistics is essential.

Qualifications

• Translate business problems into data science and ML problems with clear success metrics.
• Apply statistical analysis and experimentation techniques for data-driven decisions.
• Develop, evaluate and improve ML models using appropriate algorithms and feature engineering.
• Perform complex SQL-based data analysis and investigate data quality issues.
• Build reproducible, production-quality data science workflows using Python/R and Git.
• Own projects end-to-end from data discovery to deployment and monitoring.
• Partner with stakeholders to communicate findings and influence decisions.
• Identify data, model, privacy, bias risks and develop mitigation strategies.
• Communicate complex results clearly to technical and non-technical audiences.
• Contribute to data science practices and reusable frameworks.

Responsibilities

• Translate ambiguous problems into machine learning tasks with measurable success metrics.
• Experiment and test hypotheses to guide product decisions.
• Develop, validate, and productionize ML models with feature engineering.
• Conduct SQL-based analyses and ensure data quality across models.
• Deliver end-to-end data science projects from discovery to monitoring.

Skills

Python

R

SQL

Data analysis

Experimentation

Communication

Education

Bachelor's or Master's in CS/DS/Statistics/Math/Engineering

Tools

Git

Docker

Kubernetes

PyTorch

TensorFlow

Spark

Airflow

Job description

Role & Responsibilities

• Translate ambiguous business and product problems into well-defined data science and machine learning problems, with clear success metrics and evaluation criteria.
• Apply statistical analysis and experimentation techniques to support data-driven decision-making, including hypothesis testing, A/B testing, and causal analysis.
• Develop, evaluate, and improve machine learning models using appropriate algorithms, metrics, validation techniques, and feature engineering approaches.
• Perform complex SQL-based data analysis, investigate data quality issues, and work with structured data models to derive meaningful insights.
• Build reproducible and production-quality data science workflows using Python/R, Git, testing, documentation, and appropriate development practices.
• Own projects end-to-end, from data discovery and problem definition through modeling, validation, deployment planning, and monitoring.
• Partner with business, product, engineering, and other stakeholders to understand requirements, communicate findings, and influence data-driven decisions.
• Identify risks related to data availability, model performance, privacy, bias, and feedback loops, and proactively develop mitigation strategies.
• Communicate complex analytical results and model outputs in a clear and actionable manner to both technical and non-technical stakeholders.
• Contribute to improving data science practices, reusable frameworks, model quality, and analytical standards across the team.

Preferred Candidate Profile

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
• Strong hands-on experience in Python or R, with a solid understanding of data analysis and machine learning workflows.
• Strong knowledge of statistics, experimentation, hypothesis testing, A/B testing, and causal inference.
• Strong proficiency in SQL, including complex joins, CTEs, window functions, and data validation.
• Practical experience developing and evaluating supervised machine learning models, including regression, tree-based models, boosting, and regularization.
• Good understanding of model evaluation, feature engineering, cross-validation, data leakage, imbalanced datasets, and model interpretability.
• Experience taking machine learning/data science projects from problem definition to implementation and business delivery.
• Strong analytical and problem-solving ability, with a product-oriented and business-focused mindset.
• Excellent communication and stakeholder management skills, with the ability to explain technical concepts and business impact clearly.
• Experience with Git and reproducible development practices.

Good to Have

• Experience with MLOps, model deployment, monitoring, CI/CD, Docker, or Kubernetes.
• Exposure to AWS, Azure, or GCP cloud-based ML platforms.
• Experience with PyTorch/TensorFlow, NLP, Computer Vision, recommender systems, or transformers.
• Knowledge of Spark, Databricks, Airflow, Kafka, or other data engineering technologies.
• Experience with advanced causal inference, optimization, forecasting, or reinforcement learning.
• Relevant industry/domain experience such as fintech, healthcare, supply chain, pricing, fraud/risk, or similar areas.
• Experience mentoring team members or contributing to data science best practices and reusable frameworks.