Machine Learning Intern

About the internship

We are seeking a Machine Learning Engineer Intern with a strong foundation in machine learning and deep learning, particularly for structured and tabular data. The intern will build, evaluate, explain, and integrate predictive models using customer, transaction, financial, and operational datasets.

Selected intern's day-to-day responsibilities include:

1. Clean and analyze tabular datasets, addressing missing values, outliers, categorical variables, and data quality issues.
2. Perform exploratory data analysis and feature engineering, and build classification and regression models against clear baselines.
3. Develop and compare tree-based ensemble models and neural networks for tabular prediction, and package trained models for repeatable inference.
4. Design appropriate training, validation, and test splits while identifying and preventing data leakage.
5. Evaluate models using suitable metrics, cross-validation, and error analysis.
6. Build reproducible Python pipelines for data preparation, training, and inference; document experiments, explain predictions, and collaborate with developers on application integration.

Who can apply

Only those candidates can apply who

• are available for the work from home job/internship
• can start the work from home job/internship between 15th Oct'26 and 19th Nov'26
• are available for duration of 4 months
• have relevant skills and interests
• * Women wanting to start/restart their career can also apply.

Stipend

INR₹ 20,000 - 30,000 /month

Deadline

2026-11-15 23:59:59

Other perks

Certificate, Letter of recommendation

Skills required

Python, Machine Learning, Natural Language Processing (NLP), Deep Learning and Artificial intelligence

Other requirements

• required skills

• 1. Strong Python skills (pandas, NumPy, scikit-learn), with working knowledge of SQL and Git.
• 2. Understanding of probability, statistics, and linear algebra.
• 3. Knowledge of regression, classification, decision trees, random forests, and gradient boosting, with hands-on experience in XGBoost, LightGBM, or CatBoost.
• 4. Understanding of neural networks, backpropagation, loss functions, optimization, and regularization, with practical experience in PyTorch or TensorFlow.
• 5. Knowledge of feature encoding, class imbalance, overfitting, cross-validation, and model evaluation.
• 6. At least one end-to-end tabular ML project and experience training a neural network.

• good to have

• 1. Exposure to TabNet, FT-Transformer, TabPFN, or TabICL.
• 2. Familiarity with SHAP, probability calibration, synthetic data, hyperparameter tuning, experiment tracking, or model deployment.
• 3. Interest in credit risk, churn, customer propensity, collections, or forecasting.

• career progression

• Potential pathway to a full-time Machine Learning Engineer role, subject to performance and business requirements.

• eligibility

• Students or recent graduates in Statistics, Mathematics, Engineering, Computer Science, AI/ML, or related quantitative fields.

About company

At dSights, we empower ambitious businesses, especially those underserved by traditional tech, with outcome-driven data science and AI solutions. Founded to democratize data, we've become a trusted partner by focusing on real results, not just technology.

Combining advanced analytics, platform engineering, and seamless deployment, we help organizations make smarter decisions and extract deeper insights. As AI evolves, we continue to innovate unlocking the full potential of data through bold experimentation.