Data Scientist

Systems Limited; India; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Systems Limited in India is seeking an experienced Data Scientist to build, evaluate, and productionize predictive and intelligent models across ML, DL, NLP, and computer vision. The role emphasizes production-ready solutions, rigorous statistics, and translating insights into business outcomes.

You will own end-to-end model development, feature engineering, experiment tracking, and collaboration with ML Engineers to deploy and monitor models in production while mentoring junior team members.

Qualifications

• 5+ years of applied Data Science experience with production model-shipping.
• Strong expertise in classical/statistical ML, forecasting, optimization, and deep learning.
• Experience with NLP, Computer Vision, graph-based features, or knowledge graphs is a plus.
• Strong Python, SQL, and experience with scikit-learn, PyTorch/TensorFlow, MLflow, or Weights & Biases.
• Strong knowledge of hypothesis testing, confidence intervals, and causal inference.
• Familiarity with fairness/bias evaluation and explainability tools such as SHAP and LIME.
• Strong communication skills with the ability to explain technical concepts and model trade-offs to business stakeholders.
• Ability to defend modeling decisions and collaborate closely with engineering and architecture teams.

Responsibilities

• Own model development, experimentation, evaluation, and production readiness.
• Develop forecasting, optimization, recommendation, decision-science, and ML models.
• Perform feature engineering, EDA, and statistical analysis to identify meaningful patterns.
• Design rigorous model evaluation, validation, and testing frameworks.
• Validate model performance, fairness, and bias before go-live.
• Manage experiment tracking, datasets, and model reproducibility using tools such as MLflow/W&B.
• Present model results, trade-offs, and uncertainty to business stakeholders.
• Collaborate with ML Engineers and Architects to productionize models.
• Lead complex decision-intelligence engagements when required.
• Mentor junior Data Scientists and ML Engineers.

Skills

5+ years experience

Python

SQL

ML/DL

NLP

Computer Vision

scikit-learn

PyTorch

TensorFlow

MLflow/W&B

Tools

MLflow

Weights & Biases

scikit-learn

PyTorch

TensorFlow

Job description

We are looking for an experienced Data Scientist to build, evaluate, and productionize predictive and intelligent models across ML, deep learning, NLP, and computer vision. The role focuses on developing production-ready models, maintaining statistical rigor, and translating insights into business outcomes.

Responsibilities:

• Own model development, experimentation, evaluation, and production readiness.
• Develop forecasting, optimization, recommendation, decision-science, and ML models.
• Perform feature engineering, EDA, and statistical analysis to identify meaningful patterns.
• Design rigorous model evaluation, validation, and testing frameworks.
• Validate model performance, fairness, and bias before go-live.
• Manage experiment tracking, datasets, and model reproducibility using tools such as MLflow/W&B.
• Present model results, trade-offs, and uncertainty to business stakeholders.
• Collaborate with ML Engineers and Architects to productionize models.
• Lead complex decision-intelligence engagements when required.
• Mentor junior Data Scientists and ML Engineers.

Qualifications:

• 5+ years of applied Data Science experience with production model-shipping experience.
• Strong expertise in classical/statistical ML, forecasting, optimization, and deep learning.
• Experience with NLP, Computer Vision, graph-based features, or knowledge graphs is a plus.
• Strong Python, SQL, and experience with scikit-learn, PyTorch/TensorFlow, MLflow, or Weights & Biases.
• Strong knowledge of hypothesis testing, confidence intervals, and causal inference.
• Familiarity with fairness/bias evaluation and explainability tools such as SHAP and LIME.
• Strong communication skills with the ability to explain technical concepts and model trade-offs to business stakeholders.
• Ability to defend modeling decisions and collaborate closely with engineering and architecture teams.