Senior Data Scientist || Remote/WFH || 4 Months + Extendable Contract

Job Title: Senior Data Scientist

Experience Required: 10+ Years

Location: India (Remote)

Contract Duration: Initial 4 Months (High possibility of extension based on performance and project requirements)

Start Date: June 1, 2026

We are looking for an experienced Data Scientist to join our team and develop advancedanalytics, machine learning, forecasting, anomaly detection, entity resolution, and dataintelligence solutions for an enterprise data platform. The ideal candidate will have stronghands-on experience in statistical analysis, machine learning, Python, SQL, data profiling,predictive modeling, and business problem solving.

The role will focus on improving data quality, standardizing business entities, buildingforecasting models, identifying anomalies, and generating actionable insights for businessteams.

Key Responsibilities

Analyze large-scale business datasets, including POS, sales, customer, distributor, product,territory, finance, and operational data.

Perform detailed data profiling to identify missing values, duplicates, outliers, inconsistentformats, grain differences, and data quality gaps.

Develop machine learning models for entity resolution, customer/product/distributormatching, data imputation, anomaly detection, segmentation, and forecasting.

Design and implement POS file similarity cohorting models to group similar file structures andsupport reusable ingestion/mapping templates.

Build Named Entity Resolution solutions to standardize customer, distributor, product,territory, and channel names against master data.

Develop field-level data imputation strategies using rule-based, statistical, and ML-basedmethods.

Create data quality scoring frameworks, dataset health scores, and business readinessmetrics.

Build sales forecasting, demand forecasting, and sales landing/projection models usingstatistical, machine learning, and ensemble techniques.

Develop anomaly detection models to identify unusual movements in sales, quantity, revenue,unit price, distributor performance, and forecast variance.

Perform customer and distributor segmentation using RFM, clustering, growth-value analysis,and opportunity scoring.

Evaluate third-party datasets for business relevance, coverage, joinability, quality, andforecasting value.

Collaborate with business stakeholders to understand KPI definitions, business rules,reporting requirements, and analytical use cases.

Work with data engineers to convert model requirements into scalable data pipelines andproduction-ready feature datasets.

Work with ML engineers to transition models from experimentation to production deployment.

Define model evaluation metrics such as precision, recall, F1-score, MAPE, WAPE, RMSE,bias, accuracy, and business impact measures.

Develop clear model documentation, methodology notes, assumptions, limitations, andbusiness interpretation.

Support ontology, semantic layer, and NLQ initiatives by helping define business entities,relationships, metrics, and data rules.

Create dashboards, reports, and analytical summaries to communicate findings to technicaland non-technical stakeholders.

Continuously monitor model performance, data drift, feature drift, and business metricchanges.

Qualifications & Skills

5+ years of experience in data science, machine learning, advanced analytics, or statisticalmodeling.

Strong hands-on experience with Python, pandas, NumPy, scikit-learn, SciPy, statsmodels,and Jupyter notebooks.

Strong SQL skills for data extraction, transformation, validation, and analytical querying.

Experience in exploratory data analysis, statistical profiling, data quality assessment, and datacleansing.

Hands-on experience with supervised and unsupervised machine learning models such asLogistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, K-Means, DBSCAN, andIsolation Forest.

Experience with forecasting models such as moving average, exponential smoothing,ARIMA/SARIMA, Prophet-style models, XGBoost/LightGBM forecasting, and ensembleforecasting.

Experience with entity matching techniques such as fuzzy matching, token similarity,embeddings, pairwise classification, and confidence scoring.

Experience with anomaly detection, outlier detection, segmentation, and pattern recognition.

Strong understanding of feature engineering, model selection, cross-validation,hyperparameter tuning, and model evaluation.

Experience working with business datasets such as POS, sales, finance, customer master,product master, distributor data, CRM data, or supply chain data.

Good understanding of data warehousing concepts, dimensional models, fact/dimensiontables, and Bronze/Si