Aakash M

Associate Business Analyst

Chennai 4+ yrs exp 89 · Excellent

About

Data Scientist with 4.8+ years of experience designing and implementing Machine Learning, Predictive Analytics, Statistical Modeling, and Generative AI solutions across pharmaceutical and enterprise domains. Experienced in developing end-to-end data science workflows including data acquisition, exploratory data analysis, feature engineering, model development, validation, forecasting, experimentation, recommendation systems, and advanced analytics. Strong expertise in Python, SQL, Machine Learning, Deep Learning, LLMs, RAG, Databricks, Snowflake, AWS, Azure, and Power BI. Proven ability to transform large-scale data into actionable insights that drive business growth, operational efficiency, and strategic decision-making.

Skills & Expertise (45)

Python Advanced

8.5/10

4.8

Years Exp

Machine Learning Advanced

8.4/10

4.8

Years Exp

SQL Advanced

8.0/10

4.8

Years Exp

Deep Learning Intermediate

7.6/10

4.8

Years Exp

Natural Language Processing Advanced

7.5/10

4.8

Years Exp

Data Pipelines Advanced

7.0/10

4.8

Years Exp

Azure Intermediate

7.0/10

4.8

Years Exp

AWS Intermediate

7.0/10

4.8

Years Exp

Generative AI Intermediate

6.5/10

4.8

Years Exp

Oracle SQL Business Intelligence Data Warehousing Data Modeling Data Transformation Data Cleaning Databricks Snowflake Exploratory data analysis Statistical Analysis Hypothesis Testing A/B testing KPI Analytics Data Visualization Dashboard Development Power BI Reporting Automation Feature Engineering Large Language Models Retrieval-Augmented Generation Predictive Analytics Statistical Modeling Classification Algorithms Regression Models Clustering Techniques Recommendation Systems Time Series Forecasting ETL Development Feature Selection Hyperparameter Tuning Model Evaluation Prompt Engineering Pandas NumPy scikit-learn Matplotlib

Work Experience

Business Analyst

Navitas Life Sciences

Oct 2023 - Feb 2026

Developed predictive machine learning solutions on large-scale pharmaceutical datasets containing 100K+ records to identify business opportunities, forecast trends, and improve commercial performance. Designed end-to-end machine learning workflows including data extraction, preprocessing, feature engineering, model development, validation, and performance optimization. Built predictive models for sales forecasting, trend prediction, customer segmentation, and business performance optimization. Applied advanced statistical analysis, hypothesis testing, and predictive analytics techniques to identify key business drivers and support data-driven decision-making. Engineered machine learning-ready datasets by integrating and transforming structured enterprise data from multiple business systems. Developed scalable ETL pipelines using Python and SQL to support advanced analytics and machine learning initiatives. Performed exploratory data analysis (EDA), anomaly detection, segmentation analysis, and trend analysis on pharmaceutical datasets. Conducted comparative model analysis and experimentation to evaluate model performance and improve prediction accuracy. Built automated reporting frameworks and analytical solutions that improved efficiency and scalability of business insights. Developed interactive Power BI dashboards to communicate machine learning outputs, business insights, and predictive recommendations. Utilized Databricks, Snowflake, AWS, and Azure environments for scalable analytics and machine learning workflows. Collaborated with cross-functional teams including business, product, marketing, and technology stakeholders to deliver data science solutions. Presented analytical findings, predictive insights, and model recommendations to leadership teams and business stakeholders.

Data Scientist

Alfa Information Technologies

Jul 2021 - Oct 2023

Developed machine learning models using Logistic Regression, Linear Regression, Random Forest, Decision Trees, XGBoost, KNN, and Ensemble Learning techniques. Built predictive analytics solutions for customer churn prediction, customer behavior analysis, forecasting, and business intelligence applications. Conducted exploratory data analysis (EDA) to identify patterns, anomalies, business trends, and predictive signals. Performed feature engineering, feature selection, and hyperparameter tuning to improve model accuracy and performance. Designed automated machine learning pipelines for data extraction, cleansing, transformation, feature generation, and reporting. Built scalable ETL workflows integrating data from multiple enterprise systems while maintaining data quality and consistency. Developed recommendation systems using collaborative filtering and similarity-based recommendation algorithms. Evaluated machine learning models using Accuracy, Precision, Recall, F1-Score, ROC-AUC, RMSE, and Cross-Validation techniques. Created Power BI dashboards and interactive visualizations to communicate model outputs and business insights. Automated reporting processes using Python, reducing manual effort and improving operational efficiency. Documented machine learning methodologies, feature engineering approaches, model assumptions, and project outcomes.