Data Scientist

Location: XenonStack office, Sector 75, STPI, Mohali (Chandigarh Tricity), work from office

Experience: 3–6 years | Employment: Full time, XenonStack Private Limited | Team: AI / Machine Learning / Data Science

ABOUT XENONSTACK

• XenonStack is a Data and AI Foundry for Agentic Systems, enabling enterprises to design, deploy, operate, and scale intelligent agents across digital and physical environments. We build enterprise-grade platforms across the agentic stack:
• Akira AI – Reasoning and agent orchestration.
• ElixirData – Agentic analytics intelligence. Explainable, decision-centric analytics for measurable business outcomes.
• NexaStack – Agentic infrastructure automation. Secure, compliant AI deployment across cloud, edge, and on-prem.
• MetaSecure – Trust, compliance and defense. Continuous assurance with AI-BOMs, risk scoring, and agentic security.
• Our mission is to accelerate the world's transition to AI + Human Intelligence by making agentic systems reliable, responsible, and enterprise-ready.

ABOUT THE ROLE

We are looking for a Data Scientist with strong expertise in statistical analysis, machine learning, predictive modelling and Python. You will build end-to-end data science solutions on real-world data and turn business problems into measurable, data-driven outcomes, working with data engineers, AI/ML engineers, software developers and business stakeholders to design, evaluate and operationalise machine learning models.

ROLE MISSION

Own data science problems end to end: pick the right approach, prove it against a baseline, ship it into a product or workflow, and show the business value it delivers.

KEY RESPONSIBILITIES

• Analyse complex structured and unstructured datasets to find patterns, trends and business opportunities.
• Build machine learning models for classification, regression, clustering, forecasting, anomaly detection and recommendation.
• Perform exploratory data analysis, feature engineering, data preprocessing and feature selection.
• Apply statistical techniques: hypothesis testing, probability distributions, regression analysis and experimentation.
• Select, train, tune, validate and benchmark algorithms against appropriate baselines and evaluation metrics.
• Build reusable Python data science components and reproducible modelling pipelines.
• Work with engineering teams to integrate models into applications through APIs or batch workflows.
• Evaluate model accuracy, reliability, interpretability and performance on real-world data; monitor behaviour and contribute to retraining and drift analysis.
• Research and evaluate emerging ML, deep learning and Generative AI techniques for relevant business problems.
• Communicate findings, technical trade-offs and recommendations clearly to stakeholders.

HOW WE WORK WITH AI

• AI is part of the daily workflow at XenonStack, not an add-on.
• At our Mohali/Chandigarh office, AI tools are open for use: coding assistance, analysis, experiment design, documentation and research.
• When working on a client's data or environment, you follow the client's compliance and information security policy: use only client-approved tools, and never put client data, code, credentials or configurations into any tool the client has not approved.
• Every AI-assisted output is reviewed, tested and understood by you before it is relied on or shipped.
• Come ready to talk about what is in your AI stack, how you verify its output, and how you keep it compliant.

WHAT SUCCESS LOOKS LIKE

• Models that beat a clear baseline on agreed metrics and hold up on real-world data.
• Pipelines that are reproducible: anyone on the team can retrain and get the same result.
• Models integrated into products or workflows, with monitoring for drift and performance.
• Stakeholders who understand what a model does, how well it works, and what value it delivered.

MUST-HAVE SKILLS

Python and data: strong hands-on Python; NumPy, Pandas, Scikit-learn and visualisation libraries; strong SQL (joins, aggregations, CTEs, analytical queries); handling missing values, outliers, imbalanced and large datasets.

Statistics and mathematics: probability, statistics, linear algebra and optimisation; hypothesis testing, significance, confidence intervals and regression analysis.

Machine learning: supervised and unsupervised learning; regression, classification, decision trees, random forests, gradient boosting and clustering; time-series forecasting; feature engineering, hyperparameter tuning, model selection and cross-validation; overfitting, regularisation, data leakage and generalisation; metrics such as precision, recall, F1, ROC-AUC, MAE, RMSE and R².

Software engineering and production: reusable, maintainable Python; Git and unit testing; model integration through REST APIs; exposure to deploying, maintaining or monitoring models in production.

GOOD-TO-HAVE SKILLS

• PyTorch or TensorFlow
• NLP, transformers, embeddings and Large Language Models
• Generative AI, Retrieval-Augmented Generation (RAG) or LLM evaluation
• Recommendation systems or advanced anomaly detection
• Geospatial and route/location data (GeoPandas, PostGIS, OR-Tools)
• Optimisation techniques: resource allocation and scheduling
• MLOps: MLflow, Docker, CI/CD
• Apache Spark, PySpark, Databricks or distributed data processing
• Cloud ML platforms on AWS, Azure or Google Cloud
• Model explainability, bias assessment, drift monitoring and responsible AI practices

CANDIDATE PROFILE

3–6 years of relevant experience in Data Science, Machine Learning or Applied AI, with proven hands-on model development and validation in Python.

End-to-end project experience, from problem definition and data preparation to evaluation and integration; at least one substantial ML project delivered for a real-world use case, preferably in production.

Bachelor's degree in Computer Science, Data Science, AI, Mathematics, Statistics, Engineering or a related quantitative field; a relevant Master's is an advantage. Demonstrated competence and project work count alongside formal qualifications.

Strong analytical thinking, clear written and verbal communication, and the ability to work independently and across engineering and business teams.

Curiosity, continuous learning, and care for model quality and reproducibility.

We value people who show real ownership of data science problems rather than applying prebuilt algorithms. Be ready to explain why you chose a model, how you evaluated it, what went wrong with the data, and how your solution delivered measurable business value.

HIRING PROCESS

Pre-screening assessment (about 10 minutes, at application) → Technical interview → Face-to-face technical assessment at our Mohali office → HR and Leadership round → Background verification → Offer.

TERMS

• Location: work from our Mohali (Chandigarh Tricity) office.
• Full time; immediate joiners or short notice periods preferred.
• Background verification (BGV) is mandatory.
• Compensation as per company policy and budget band.

HOW WE WORK

We value deep work, clarity, and execution over activity. You will be expected to clarify, plan, execute, and escalate with options, not wait to be told. Ownership sits with the individual, and outcomes are measured, not effort.

OUR VALUES

Agency – Be self-directed and proactive | Taste – Sweat the details and build with precision | Ownership – Take responsibility for outcomes | Mastery – Commit to continuous learning and growth | Impatience – Move fast and challenge inertia | Customer Obsession – Always put the customer first

WHY JOIN US

• Work on real enterprise data and ship models into AI platforms used by global clients.
• AI-first environment: work alongside the agentic platforms we build.
• Great Place to Work-Certified, 14+ years, headquartered in Mohali.