Data Science - Internship (WFH)

About the internship:As a Data Science intern at Applied Generative Solutions, you will have the opportunity to build on and go very deep in the areas of statistical and machine learning modeling. You will be working on finding complex patterns in massive sales and marketing data using advanced statistical and machine learning techniques.

Apply if you have strong machine learning and statistical modeling background and curiosity and interest in digging deep in the data to find meaningful patterns.

Good Knowledge of at least some of the below is required:

Supervised and survival modelling (churn risk)

Gradient-boosted trees (LightGBM / XGBoost) and logistic regression; handling class imbalance (stratified sampling, class weights); calibration curves and per-segment calibration

SHAP values - where and how they are used

Survival analysis: Kaplan-Meier curves, Cox proportional hazards

Temporal validation (train on past, test on future), leakage audits, population-stability index (PSI) for drift

Hierarchical and causal methods

Time-series and anomaly detection (KPI anomaly, divergence, forecasting)

Seasonal decomposition (STL, Prophet), rolling regression / Kalman filtering, residual z-scoring, ACF and stationarity tests

Hierarchical time-series forecasting with reconciliation across market region global

Decomposition and unsupervised methods (driver ranking, route mix, market clusters)

k-means clustering with silhouette and gap statistics, PCA, Mahalanobis distance for outlier and drift detection

Association rules / item-to-item recommenders for basket cross-sell; Monte Carlo simulation for ROI ranking under uncertainty

Key responsibilities include:

1. Collaborating with senior data scientists to analyze and interpret complex datasets

2. Building predictive models and algorithms to extract valuable insights

3. Assisting in the development and implementation of data-driven strategies

4. Conducting thorough data analysis to identify trends and patterns

5. Communicating findings and recommendations to stakeholders

6. Exploring new technologies and techniques to enhance data analysis processes

7. Contributing to the overall success of our data science initiatives through your innovative ideas and problem-solving skills.Who can apply:Only those candidates can apply who:are available for the work from home job/internshipcan start the work from home job/internship between 27th Sep'26 and 1st Nov'26are available for duration of 4 monthshave relevant skills and interestsStipend:INR₹ 25,000 - 50,000 /monthDeadline:[hidden]:59:59Other perks:Certificate, Letter of recommendation, Flexible work hours, 5 days a weekSkills required:Python, SQL, Data Analytics, Statistical Modeling, Machine Learning and Data ScienceOther Requirements:1. Business-aware EDA: Ability to distinguish seasonality from trends, correlation vs causation2. Predictive modeling: seasonal forecasting, calibrated classification and customer-value models, feature ablation and precision at actual business capacity.3. Statistical diagnosis: Be able to quickly learn or aware of statistical techniques such as Kaplan–Meier estimation, Cox or discrete-time survival models, and hierarchical/mixed-effects models to explain repurchase, churn and sponsor-performance differences with appropriate uncertainty.4. Causal decision intelligence: Ability to design randomized experiments and apply propensity-score methods, doubly robust estimation and uplift modeling, explicitly testing assumptions before recommending actions or claiming incremental business impact.pply only if you have deep understanding of how5. Strong SQLskills to validate customer identities, table grain, joins, duplicates, missing values, cancellations and returns before building modeling datasets.About Company:IT and Technology related products and services in the areas of Artificial Intelligence, Generative AI, Deep Learning, Data Science and Machine Learning, Cloud and Data Engineering.

Responsibilities

As a Data Science intern at Applied Generative Solutions, you will have the opportunity to build on and go very deep in the areas of statistical and machine learning modeling. You will be working on finding complex patterns in massive sales and marketing data using advanced statistical and machine learning techniques.

Apply if you have strong machine learning and statistical modeling background and curiosity and interest in digging deep in the data to find meaningful patterns.

Good Knowledge of at least some of the below is required:

Supervised and survival modelling (churn risk)

Gradient-boosted trees (LightGBM / XGBoost) and logistic regression; handling class imbalance (stratified sampling, class weights); calibration curves and per-segment calibration

SHAP values - where and how they are used

Survival analysis: Kaplan-Meier curves, Cox proportional hazards

Temporal validation (train on past, test on future), leakage audits, population-stability index (PSI) for drift

Hierarchical and causal methods

Time-series and anomaly detection (KPI anomaly, divergence, forecasting)

Seasonal decomposition (STL, Prophet), rolling regression / Kalman filtering, residual z-scoring, ACF and stationarity tests

Hierarchical time-series forecasting with reconciliation across market region global

Decomposition and unsupervised methods (driver ranking, route mix, market clusters)

k-means clustering with silhouette and gap statistics, PCA, Mahalanobis distance for outlier and drift detection

Association rules / item-to-item recommenders for basket cross-sell; Monte Carlo simulation for ROI ranking under uncertainty

Key responsibilities include:

1. Collaborating with senior data scientists to analyze and interpret complex datasets

2. Building predictive models and algorithms to extract valuable insights

3. Assisting in the development and implementation of data-driven strategies

4. Conducting thorough data analysis to identify trends and patterns

5. Communicating findings and recommendations to stakeholders

6. Exploring new technologies and techniques to enhance data analysis processes

7. Contributing to the overall success of our data science initiatives through your innovative ideas and problem-solving skills.