Data Science Lead/Manager- Immediate Joiner

Techhire Global; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Techhire Global seeks a senior data science leader in Bengaluru to mentor a team of data scientists and analysts. You will drive the development of predictive, prescriptive, and generative AI models, while establishing end-to-end methodologies and governance across the data lifecycle.

You will partner with AI/ML engineers to deploy models in production, monitor KPIs for accuracy, latency, and fairness, and deliver data-driven solutions for government services and citizen engagement.

Qualifications

• 10-12 years of total experience in data science and analytics.
• 6-8 years in advanced analytics, statistical modelling, and research-driven problem solving.
• Minimum 4-6 years leading data science teams or projects involving large-scale model deployment.
• Proven track record in implementing AI/ML models in production environments within enterprise or government setups.

Responsibilities

• Lead and mentor a team of Data Scientists and Analysts in developing predictive, prescriptive, and generative AI models.
• Establish end-to-end data science methodologies, including feature engineering, model selection, and performance evaluation.
• Develop data-driven solutions for policy optimization, citizen engagement, and government service enhancement.
• Define data model lifecycle standards, including model versioning, retraining, and explainability frameworks.
• Collaborate with AI/ML Engineers to operationalize ML models in production systems. Define and monitor AI performance KPIs such as accuracy, latency, and fairness.

Skills

Python

R

SQL

Deep Learning

NLP

Computer Vision

Time Series

PyTorch

Transformer

Education

BTech / MTech / MS / PhD in STEM

AWS ML Specialty (preferred)

Tools

Spark

Databricks

Hadoop

Hive

MLflow

DVC

Tableau

Power BI

Job description

Role & responsibilities

1.Lead and mentor a team of Data Scientists and Analysts in developing predictive,prescriptive, and generative Al models.
2.Establish end-to-end data science methodologies, including feature engineering, model selection, and perfomance evaluation..

3. Develop data-driven solutions for policy optimization, citizen engagement, and government service enhancement.
4.Define data model lifecycle standards, including model versioning, retraining, and explainability frameworks.
5. Collaborate with AI/ML Engineers to operationaliz machine learning models within production systems. Define and monitor Al performance KPIs such as accuracy, latency, and fairness.

Preferred candidate profile

Educational Qualification

• B.Tech / M. Tech / M.S. / Ph.D. in Statistics, Mathematics, Computer Science, or any quantitative discipline.
• Advanced certifications in Data Science, Machine Learning, or AI (e.g. AWS ML Specialty) preferred.
• Research papers, case studies, or significant open source contributions(are preferred)

Experience

• 10-12 years of total experience in data science and analytics.
• 6-8 years in advanced analytics, statistical modelling, and research-driven problem solving.
• Minimum 4-6 years leading data science teams or projects involving large-scale model deployment.
• Proven track record in implementing AI/ML models in production environments within enterprise or government setups.

Technical Competencies

• Programming & Tools: Python (pandas, NumPy, scikit-learn, PyTorch, TensorFlow), R, SQL.
• Advanced ML Techniques: Deep learning, ensemble methods, time series forecasting, NLP, computer vision, reinforcement learning, and causal inference
• Al Techniques: Deep learning, NLP (Transformers, BERT, GPT), computer vision, anomaly detection.
• Big Data & Analytics: Apache Spark, Databricks, Hadoop, Hive, ELT pipelines.
• Visualization: Tableau, Power BI, Matplotlib, Seaborn. Research & Innovation: Literature review, research methodology, and evaluation Of emerging AI technologies
• Model Lifecycle Management: MLflow, Data Version Control (DVC), and experiment tracking frameworks.
• Model Evaluation: Advanced metrics, cross-validation strategies, bias detection, fairness evaluation, and model intemretability
• Governance: Responsible AI design, model interpretability, and privacy-preserving AI (DP, FL)