Rajashekar Reddy Alwala
Summary
Results-driven AI Specialist with 11 years of experience in advanced analytics and data science across diverse sectors, including financial services and telecommunications. Expertise spans the complete data science lifecycle, statistical modeling, and machine learning techniques. Proficient in Python and R, with strong SQL skills and hands-on experience in AWS cloud platforms. Skilled in developing scalable, data-driven solutions and impactful dashboards in Agile environments.
Overview
11
11
years of professional experience
Work History
Data Scientist | AI/ML Engineer
Mastercard
04.2024 - Current
- Designed a natural language-driven analytics interface enabling non-technical users to retrieve insights from large transactional datasets without writing SQL, improving self-service analytics adoption across business teams.
- Developed retrieval-augmented workflows to combine enterprise data with large language models for contextualized analytical responses and decision support.
- Evaluated and integrated AWS-native generative AI services and vector storage technologies to support enterprise-grade retrieval-augmented analytics solutions.
- Built semantic search pipelines using text embeddings to support contextual retrieval across structured and semi-structured datasets, reducing dependency on predefined reports.
- Architected AWS-hosted AI solutions integrating object storage, serverless computing, and managed ML services to support scalable inference and experimentation.
- Implemented vector-based indexing strategies to optimize similarity search and downstream AI inference workloads in cloud environments.
- Collaborated with senior data scientists to understand business context and data behavior, informing modeling approaches in payments and financial services.
- Contributed to a customer segmentation initiative using machine learning and statistical modeling, generating data products to support targeted marketing and personalization strategies.
- Built, evaluated, and refined predictive models using Python and R, improving model accuracy, efficiency, and scalability.
- Performed data manipulation, cleansing, normalization, and feature engineering to prepare datasets for analytical and predictive modeling.
- Automated manual data processing workflows using big data technologies such as Apache Spark, Python, and AWS services, significantly improving processing efficiency.
- Developed on-demand analytical tables from Amazon S3 data using AWS Lambda, AWS Glue, Python, and PySpark.
- Designed and implemented recommendation systems using collaborative filtering techniques to deliver personalized product and service recommendations, deployed on AWS EMR.
- Analyzed and prepared large-scale transactional and customer datasets to identify trends and patterns using historical and predictive modeling techniques.
- Developed and deployed multiple analytics solutions to production environments using CI/CD pipelines, supporting real-time data ingestion, storage, and analytics.
- Implemented CI/CD automation using GitLab pipelines to orchestrate analytics and model deployments across development, test, and production environments.
- Led data ingestion and transformation pipelines migrating sensitive on-prem relational data into secure AWS environments with encryption and role-based access controls.
- Applied data masking and anonymization techniques to protect regulated PII while enabling advanced analytics and machine learning use cases.
- Supported data platform modernization efforts, including migration of data objects from Teradata to Snowflake.
- Built price elasticity models to evaluate pricing sensitivity for bundled products and services, supporting revenue optimization strategies.
- Developed predictive causal models using historical failure rates and cost data to evaluate and forecast performance of new bundled service offerings.
- Environment: Python, R, SQL, Apache Spark, PySpark, AWS (S3, Glue, Lambda, EMR), Snowflake, Teradata, GitLab CI/CD, Big Data Analytics
Data Scientist | AI/ML Engineer
Delta Airlines
Atlanta
10.2020 - 03.2024
- Developed AI-driven search and discovery capabilities allowing operational teams to explore incident, operations, and customer datasets using conversational queries.
- Built and validated statistical and machine learning models aligned with business objectives such as demand forecasting, customer behavior analysis, and operational efficiency.
- Leveraged machine learning and statistical techniques such as regression, classification, clustering, and time-series analysis to generate actionable insights.
- Applied advanced NLP techniques to extract actionable insights from unstructured operational notes and free-text records.
- Performed exploratory data analysis (EDA) using Python and R, including descriptive statistics, outlier detection, correlation analysis, and trend identification on airline operational, sales, and customer datasets.
- Created domain-aware data models to represent complex entity relationships, enhancing analytical accuracy and interpretability.
- Extracted, transformed, and prepared data from relational databases using SQL, MS Excel, and MS Access, creating analysis-ready datasets in Python and R.
- Built and validated feature pipelines to support predictive modeling and pattern detection across high-volume operational records.
- Cleaned, transformed, and standardized large datasets using Python libraries (pandas, NumPy, SciPy) to improve model performance and reporting reliability.
- Conducted data profiling and data quality assessments using complex SQL queries to ensure accuracy, completeness, and consistency across datasets.
- Designed cloud-native ML workflows leveraging managed services for model training, evaluation, and deployment at scale.
- Developed and supported ETL and analytics pipelines by validating data movement from source systems to cloud and analytics platforms.
- Implemented data lineage, quality checks, and validation rules to support trust and traceability in analytical outputs.
- Worked with AWS cloud data services, including S3, AWS Glue (Catalog & Crawlers), Athena, and Lambda, to query, process, and analyze large-scale airline data.
- Partnered with engineering teams to modernize legacy analytics workflows, transitioning them into scalable, cloud-based AI pipelines.
- Collaborated with business stakeholders, product owners, and analytics teams to gather and translate business requirements into analytical and data science solutions through Joint Requirements Development (JRD) sessions.
- Partnered with BI teams to support customer and consumer analytics, ensuring insights were delivered through dashboards and reports.
- Applied domain knowledge in airline operations and customer analytics to design analytical approaches and interpret model outputs.
- Modeled and analyzed incident-driven operational records with complex entity relationships, supporting search, investigation, and trend analysis workflows.
- Environment: Python, R, SQL, MS Excel, MS Access, AWS (S3, Glue, Athena, Lambda), Tableau / BI Tools, Jenkins, Team Foundation Server
Data Analyst
State Farm
02.2017 - 09.2020
- Prepared, validated, and analyzed datasets for data analytics initiatives on insurance policy, claims, customer, and usage data to support informed decision-making.
- Conducted exploratory data analysis (EDA) to identify trends, patterns, and anomalies in customer behavior, claims activity, and service usage.
- Built analytical datasets integrating multiple relational sources to support entity resolution and relationship analysis across policyholders, claims, and incidents.
- Used SQL and MS SQL Server to extract, join, and analyze structured data from multiple insurance-related data sources.
- Performed data cleaning, transformation, and preparation using Python and R to ensure data quality and consistency for analysis and reporting.
- Used Python libraries (pandas, NumPy, matplotlib, seaborn) and R scripting to analyze datasets and create visual summaries of insights.
- Built and delivered data visualizations and dashboards in Tableau and Power BI to effectively communicate insights to business stakeholders.
- Supported early-stage predictive analytics initiatives over historical incident and claims data to identify risk patterns and inform proactive decision-making.
- Supported text and sentiment analysis efforts on customer feedback data to identify service trends and improvement areas.
- Applied basic statistical and predictive techniques to understand customer behavior and support data-driven decision-making.
- Supported analytics initiatives involving sensitive customer and claims data by implementing access controls, data obfuscation, and privacy-aware transformations.
- Assisted in designing data schemas optimized for analytical querying and downstream machine learning use cases.
- Developed exploratory models to identify behavioral patterns, anomalies, and risk indicators within structured insurance datasets.
- Resolved Tableau data extract and refresh issues, updated data sources, and republished dashboards to ensure timely and accurate reporting.
- Designed and implemented reusable data preparation frameworks to standardize ingestion, cleansing, and enrichment of structured enterprise data for consistent analysis.
- Documented analysis results, assumptions, and data definitions to support transparency and reuse of analytics work.
- Collaborated with senior data scientists to productionize analytical workflows aligned with governance and compliance requirements.
- Worked with semi-structured data formats such as JSON and XML for data ingestion and analysis.
- Environment: R, RStudio, Python, SQL, Tableau, Power BI, Hadoop, Hive, MS Access, MS Excel, Outlook
- Environment: R, RStudio, Python, SQL, Tableau, Power BI, Hadoop, Hive, MS Access, MS Excel, Outlook
Data Analyst
Cox Communications
Atlanta
02.2015 - 12.2016
- Analyzed complex relational datasets involving customer identities, service events, and usage records to support investigative and operational reporting.
- Analyzed business requirements for customer accounts, subscriptions, billing, usage records, and service history to inform data-driven decisions.
- Wrote SQL queries using T-SQL to extract, filter, and summarize data for reports and ad-hoc analysis.
- Created and maintained views and analytical datasets that enhanced dashboards and supported recurring business reports.
- Conducted data validation and reconciliation checks to ensure accuracy of customer, billing, and usage data.
- Supported unit testing and UAT activities by validating data outputs against business expectations.
- Assisted in transitioning ad-hoc reporting workflows toward more structured, analytics-ready datasets.
- Built foundational data mappings to understand relationships between entities, identifiers, and transactional records across enterprise systems.
- Worked with database and data modeling teams to understand table structures and data relationships for analysis purposes.
- Supported early data discovery efforts by enabling flexible querying and search over large operational databases.
- Collaborated with QA teams to identify, track, and verify fixes for data and reporting-related defects.
- Supported production deployments by validating reports and datasets after release.
- Assisted with data profiling and source-to-target mapping documentation to understand data flow across telecom systems.
- Partnered with engineering teams to validate data movement from source systems into centralized analytics environments.
- Used SQL Server Management Studio (SSMS) for querying, data checks, and troubleshooting.
- Worked with business users to understand reporting needs, KPIs, and metrics, and supported delivery of basic insights.
- Prepared reports, summaries, and data extracts that facilitated operational and business decision-making.
- Environment: R, RStudio, Python, SQL, Tableau, Power BI, Hadoop & Hive, MS Excel, MS Access, Outlook.
- Environment: R, RStudio, Python, SQL, Tableau, Power BI, Hadoop & Hive, MS Excel, MS Access, Outlook.
Education
Bachelor of Technology - Computer Science
JNTU
Hyderabad, India01-2013
Skills
- Machine learning techniques
- Statistical methodologies
- Data visualization tools
- Programming & Scripting: Python, R, SQL, PL/SQL, Spark
- Cloud & Analytics Platforms: AWS S3, Glue, Athena, EMR, Lambda, QuickSight
- Data engineering processes
- Model Deployment Automation
- Model Deployment
- Conversational AI techniques
- Methodologies & Tools: Agile, Scrum, SDLC, Jupyter Notebook, SSMS
- Model Deployment
Timeline
Data Scientist | AI/ML Engineer
Mastercard
04.2024 - Current
Data Scientist | AI/ML Engineer
Delta Airlines
10.2020 - 03.2024
Data Analyst
State Farm
02.2017 - 09.2020
Data Analyst
Cox Communications
02.2015 - 12.2016
Bachelor of Technology - Computer Science
JNTU