Data Scientist

wk; India; On-site; INR 900,000 - 1,500,000; Full time

Job summary

wk seeks a Data Scientist II to design, develop, test and deliver data science, ML, Generative AI, and Agentic AI solutions addressing business needs and customer experiences. You will apply statistical analysis, predictive modeling, LLMs, Retrieval-Augmented Generation, and modern AI frameworks to build reliable analytical capabilities.

This individual contributor role spans data collection, preprocessing, model development, evaluation, integration and production support, collaborating with

Qualifications

• 3+ years of professional experience in Data Science, ML, AI, analytics, or AI-focused software development.
• Hands-on data prep, exploratory analysis, feature engineering, model development and evaluation for real business problems.
• Experience contributing to ML/NLP/Generative AI/LLM/RAG/Agentic AI solutions through development, testing or production support.
• Knowledge of model validation, experiment design, performance metrics, data quality, and reproducible development practices.
• Ability to complete a range of technical tasks and collaborate with cross-functional teams while adhering to governance and security policies.

Responsibilities

• Develop data science and AI solutions across structured and unstructured data.
• Evaluate and improve model quality using metrics, testing, and monitoring.
• Support deployment with reusable code, APIs, notebooks, pipelines, and artifacts.
• Communicate insights with visuals, reports, and documentation; ensure secure, governed AI practices.

Skills

Python

SQL

Data analysis

Feature engineering

Model evaluation

Experimentation

Communication

Education

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

Tools

Pandas

NumPy

Scikit-learn

Jupyter

Matplotlib

Seaborn

Tableau

Power BI

TensorFlow

PyTorch

Hugging Face

SpaCy

LangChain

LangGraph

Azure OpenAI

Azure ML

MLflow

Git

Job description

About the Role

As a Data Scientist II, you will contribute to the design, development, testing, and delivery of data science, Machine Learning, Generative AI, and Agentic AI solutions that address business needs and improve customer experiences. You will apply statistical analysis, predictive modeling, Large Language Models, Retrieval-Augmented Generation, and modern AI frameworks to build reliable analytical and intelligent capabilities.

This individual contributor role participates across the data science lifecycle, from data collection and preprocessing through model development, evaluation, integration, and production support. Working with a moderate level of guidance, you will solve complex technical problems, collaborate with senior data scientists and cross-functional partners, and expand your knowledge of enterprise AI technologies, business processes, and customer needs.

Key Responsibilities

• Develop data science and AI solutions: Prepare and analyze structured and unstructured data, engineer features, and contribute to predictive models, NLP and LLM solutions, RAG pipelines, intelligent assistants, and Agentic AI workflows.
• Evaluate and improve solution quality: Apply suitable metrics, test datasets, error analysis, experiment tracking, model validation, performance testing, and responsible AI checks to improve accuracy, relevance, reliability, latency, and cost.
• Support deployment and engineering: Build reusable code, APIs, notebooks, pipelines, containers, model artifacts, monitoring components, and automated workflows with engineering and senior data science team members.
• Collaborate, communicate, and maintain standards: Translate requirements into assigned deliverables, communicate insights through visualizations, prototypes, reports, and documentation, and manage code, data, and experiments in line with security, privacy, governance, version control, responsible AI, and current ML and AI practices.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative field.
• 3+ years of relevant professional experience in Data Science, Machine Learning, Artificial Intelligence, analytics, or AI-focused software development.
• Hands-on experience preparing data, conducting exploratory analysis, building features, developing models, and evaluating results for practical business problems.
• Experience contributing to at least one Machine Learning, NLP, Generative AI, LLM, RAG, or Agentic AI solution through development, testing, integration, or production support.
• Working knowledge of model validation, experiment design, performance metrics, data quality, and reproducible development practices.
• Ability to complete a range of technical assignments and solve moderately complex problems within established policies, procedures, and architectural patterns.
• Effective written and verbal communication skills, including the ability to explain complex information clearly in straightforward situations.
• Strong collaboration, learning agility, attention to detail, and problem-solving skills.

Technical Skills

• Programming and analytics: Proficiency in Python and SQL, with hands-on experience in Pandas, NumPy, Scikit-learn, Jupyter, data preparation, feature engineering, analysis, and visualization using Matplotlib, Seaborn, Tableau, or Power BI.
• Machine Learning, statistics, and NLP: Practical knowledge of supervised and unsupervised learning, model selection and evaluation, statistical analysis, experimentation, and foundational deep learning and NLP concepts using frameworks such as TensorFlow, PyTorch, Hugging Face, or SpaCy.
• Generative AI and Agentic AI: Experience with prompt engineering, embeddings, vector search, RAG, LLM evaluation, document-based use cases, tool-enabled agents, and workflow orchestration using frameworks such as LangChain or LangGraph, with exposure to Azure OpenAI, Azure Machine Learning, MLflow, and model APIs.
• Engineering, cloud, and governance: Experience with reusable Python code, REST APIs, Git, unit testing, CI/CD, containers, databases, data pipelines, and cloud services, with awareness of monitoring, observability, security, privacy, cost, explainability, and responsible AI practices.

Success Measures

• Accurate, timely, and well-documented data preparation, analysis, and reporting.
• Reliable contributions to Machine Learning, Generative AI, and Agentic AI development, testing, integration, and production support.
• Measurable improvement in model or application quality, including accuracy, relevance, reliability, latency, cost, or user experience.
• Clear visualizations and actionable insights that support product and business decisions.
• Reusable, maintainable code and effective automation of repeatable data and AI workflows.
• Consistent adherence to data quality, security, governance, responsible AI, documentation, and