Apurva Mote

Data Scientist / Machine Learning

Pune, India 4+ yrs exp 89 · Excellent

About

Data professional with 4+ years of experience in Data Analytics, Business Intelligence, and applied Machine Learning at Cognizant. Strong hands-on foundation in Python, SQL, EDA, statistics, feature engineering, predictive modeling, NLP, and Power BI. Experienced in underwriting risk analytics, classification, regression, forecasting, and customer analytics, with a track record of translating business problems into data-driven solutions.

Skills & Expertise (55)

Python Advanced

8.5/10

Years Exp

Pandas Advanced

8.5/10

Years Exp

scikit-learn Advanced

8.5/10

Years Exp

Feature Engineering Advanced

8.5/10

Years Exp

SQL Advanced

8.0/10

Years Exp

Data Cleaning Advanced

8.0/10

Years Exp

Predictive Analytics Advanced

8.0/10

Years Exp

Power BI Advanced

8.0/10

Years Exp

AWS Intermediate

7.5/10

Years Exp

NLP Intermediate

7.5/10

Years Exp

Docker Intermediate

7.0/10

Years Exp

DAX Power Query Excel Tableau Matplotlib ArcGIS Seaborn KPI Reporting Prompt Engineering Data Modeling ETL ELT FastAPI Flask REST APIs Git GitHub Azure SQL Azure Data Lake Databricks EDA MySql NumPy XGBoost Random Forest Logistic Regression Linear Regression Decision Trees K-Means Feature Selection Cross-Validation Hyperparameter Tuning RAG Statistical Analysis Hypothesis Testing Outlier Detection Model Evaluation Text Preprocessing TF-IDF Sentiment Analysis Embeddings Transformers Generative AI LLMs

Work Experience

Lead - Data Analyst

Cognizant Technology Solutions

Jul 2022 - Present

Delivered 15+ enterprise Power BI dashboards used by 100+ stakeholders across HR, Finance, Sales, Operations, and executive reporting, reducing manual reporting effort by ~40%. Built and optimized SQL, ETL/ELT, Power Query, and Power BI workflows, improving reporting performance by ~30% and strengthening data quality and reliability. Performed EDA, statistical analysis, data cleaning, missing-value treatment, outlier detection, validation, reconciliation, and business analysis using Python, SQL, and Power BI. Translated business requirements into KPIs, analytical solutions, scalable data models, automated reports, and actionable recommendations for senior stakeholders. Worked on an ML-based underwriting analytics project for a global reinsurance client by integrating claims, property, census, flood-hazard, and GIS/geospatial datasets through SQL and ETL pipelines. Engineered predictive features including claim frequency, claim severity, property-risk indicators, geographic-risk scores, and flood exposure for underwriting risk analysis. Developed predictive models for geographic flood-risk estimation and insurance opportunity identification using Scikit-learn, applying feature selection, cross-validation, hyperparameter tuning, and error analysis. Used Python/R, Pandas, NumPy, Scikit-learn, ArcGIS, Matplotlib, and Seaborn for data preparation, feature engineering, modeling, visualization, and geospatial analytics. Collaborated with business, risk, product, and technical stakeholders to convert analytical and ML findings into actionable business recommendations. Combined strong Data Analytics, Business Intelligence, and Machine Learning capabilities to support end-to-end data-driven problem solving from data preparation through predictive modeling and insights.