Data Science Intern

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InternshipIntermediate foundation

Kozhikode · Hybrid

Data Science Intern

Extract actionable intelligence from complex datasets, prototype machine learning models, and build reproducible data pipelines supporting Xynvia's healthcare and enterprise AI systems.

Apply for this role 01Intermediate capability 02Real product work 03Hybrid collaboration

Role standard

Ready to contribute. Still ready to learn.

At least one end-to-end data science project or a credible portfolio demonstrating exploratory data analysis (EDA), statistical modelling, feature engineering, and model evaluation.

What matters most

Show us how you think, what you have built or learned, and how you improve work after feedback.

Responsibilities

What you will own.

1. 01Clean, explore, and preprocess structured and unstructured data to extract meaningful patterns and baseline metrics.
2. 02Develop, evaluate, and benchmark machine learning and statistical models using sound validation techniques.
3. 03Build and maintain reproducible data pipelines, ETL scripts, and analytical workflows for real-world datasets.
4. 04Collaborate with engineering teams to package and serve model prototypes via lightweight APIs (FastAPI / Flask).
5. 05Document analytical methodologies, dataset assumptions, validation results, and experiment findings with clarity.

Intermediate skills

What you should bring.

• Proficiency in Python and core data libraries (Pandas, NumPy, Scikit-Learn)
• Strong foundation in statistics, hypothesis testing, probability, and exploratory data analysis
• Intermediate SQL for querying, aggregating, and structuring relational data
• Working knowledge of machine learning algorithms (regression, classification, clustering, ensemble methods)
• Familiarity with data visualization tools (Matplotlib, Seaborn, Plotly) and version control (Git)

Useful, not required: Experience with PyTorch, TensorFlow, NLP / LLM concepts, computer vision basics, vector databases, FastAPI, Docker, or PostgreSQL is useful.

Early outcomes

What good progress looks like.

01

A validated, reproducible data pipeline or predictive model benchmarked against defined performance metrics

02

A lightweight inference API or analytical dashboard presenting model predictions and business insights

03

Clear experiment documentation, error analysis, and actionable recommendations for the production team

Hiring path

Clear and role-relevant.

01

Application review

Resume, portfolio and evidence of practical ability.

02

Working conversation

Your thinking, interests and relevant experience.

03

Practical review

A focused task or work sample when useful for the role.

04

Final alignment

Scope, expectations, location and next-step clarity.

The sequence may vary by role. Xynvia does not ask candidates to complete unpaid production work.

Interested / apply directly

Bring the work that shows your thinking.

Attach your resume and include a portfolio, GitHub, case study or relevant work sample where available.

Apply for Data Science Intern