Fresher data scientist resume example
Tanmay Kulshreshtha · Junior Data Scientist · Hyderabad, Telangana. A sample person — replace every detail with your own.
Professional summary
M.Sc. Statistics graduate with a data science internship at an insurance analytics firm, where I built a claim severity model in Python. Strong in regression, tree-based models, feature engineering and cross-validation, with working SQL and basic model deployment through Flask. Seeking a junior data scientist role on prediction or forecasting problems.
Skills
- Python (pandas, scikit-learn)
- SQL
- Regression and classification
- XGBoost and LightGBM
- Feature engineering
- Cross-validation
- Hypothesis testing
- Flask APIs
- Git
Experience
- Built a gradient boosting model to predict motor claim severity, beating the existing rule-based estimate on mean absolute error.
- Engineered features from policy, vehicle and garage data, and documented each one with its business meaning for the actuarial team.
- Wrapped the model in a Flask API and wrote unit tests so the engineering team could call it from the claims portal sandbox.
- Cleaned and analysed district-level rainfall and crop yield data for a faculty project on monsoon variability.
- Fitted mixed-effects models in R and produced charts used in a departmental working paper.
Education
Certifications and registrations
- Machine Learning Specialisation, online course
- Kaggle competitions — two bronze medals in tabular contests
Projects
- Electricity demand forecast — weekly model on public grid data with holiday and temperature features, documented on GitHub
Languages
Hindi · English
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Experienced data scientist resume example
Shruti Venkataraman · Senior Data Scientist · Bengaluru, Karnataka. A sample person — replace every detail with your own.
Professional summary
Data scientist with seven years of experience building recommendation, pricing and demand forecasting models for e-commerce and quick-commerce businesses. Takes problems from framing to production with engineers, designs A/B tests to prove impact and mentors three junior data scientists. Works in Python, SQL, Spark and MLflow on cloud data platforms.
Skills
- Machine learning in production
- Recommendation systems
- Demand forecasting
- Experiment design and causal inference
- Python, SQL, PySpark
- MLflow and model monitoring
- Feature stores
- Stakeholder storytelling
- Mentoring
Experience
- Built store-level demand forecasts for over 5,000 products, reducing stock-outs and wastage of perishables across dark stores.
- Designed the experimentation framework for pricing tests, including guardrail metrics and a minimum detectable effect calculator.
- Shipped a frequently-bought-together model that raised add-on items per order in a controlled rollout.
- Mentor three data scientists through weekly reviews of problem framing, validation choices and model documentation.
- Built a size recommendation model from returns and fit feedback, cutting size-related returns in the pilot categories.
- Developed a markdown optimisation model for end-of-season sales, tested against category managers’ manual discounts.
- Moved training pipelines from notebooks to scheduled PySpark jobs with MLflow tracking and drift alerts.
- Automated survey weighting and segmentation in Python for consumer goods clients, replacing manual spreadsheet steps.
- Built a k-means customer segmentation that a beverage client used to plan regional promotions.
Education
Certifications and registrations
- Google Cloud Professional Machine Learning Engineer
- Causal Inference for Data Science, online course
Projects
- Forecast accuracy review — built a weekly back-test report that made forecast errors visible to category teams
- Internal ML guild talk series — organised monthly sessions on experiment pitfalls and model monitoring
Languages
English · Tamil · Kannada
Download this resume
Edit it in Word or Google Docs. The layout is ATS-friendly: one column, standard headings, no tables or text boxes.
Edit this resume in the free resume builder → — change the template, colour and country, then download.
Why these resumes work
- Tanmay compares his model with the existing baseline and mentions deployment through an API, two things that separate data science from coursework on a fresher resume.
- Shruti states the domains she models for, demand, pricing and recommendations, so an e-commerce hiring manager sees direct overlap with their own problems.
- The experimentation framework bullet shows she proves impact rigorously, which matters more to senior interviewers than the name of any algorithm.
- Both resumes mention monitoring, documentation or testing, signalling that the candidate thinks about models after launch, not just about training accuracy.
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