Data Analyst with 5+ Years of Financial Analytics Experience

SRIJA JUTTU Data Analyst

Location: Pittsburgh, PA Phone: 412-***-**** Email: **********@*******.*** SUMMARY

Skilled as a Data Analyst with over 5+ years of experience delivering data-driven insights across financial services, enterprise analytics, and cloud-based platforms. Experienced in analyzing large-scale structured and semi-structured datasets, building SQL-driven reports, dashboards, and analytical models to support fraud detection, risk analysis, customer behavior insights, and operational decision-making. Proficient in Python, SQL, statistical analysis, and data visualization, with strong expertise in EDA, forecasting, segmentation, A/B testing, and KPI reporting. Demonstrated ability to translate complex data into actionable business insights, collaborate with cross- functional teams, ensure data quality and governance, and deliver analytics solutions aligned with regulatory standards and measurable business impact in Agile environments.

PROFESSIONAL EXPERIENCE

Fidelity Information Services (FIS GLOBAL) PA, USA Data Analyst Mar 2025 – Current

Performed end-to-end Data Analysis & Data Analytics on 10M+ daily financial transaction records using SQL, Advanced SQL (Joins, CTEs, Subqueries, Window Functions) and Python (Pandas, NumPy) to identify spending behavior, fraud patterns, anomalies, and operational risks, improving risk visibility by 30%.

Built fraud analytics, predictive analytics, and rule-based risk models using statistical analysis, regression analysis, hypothesis testing, and A/B testing, reducing false-positive alerts by 25% and improving decision accuracy.

Designed and automated ETL / ELT pipelines and workflow automation scripts using AWS (S3, Glue, Lambda) to support scalable data warehousing and analytics-ready datasets for dashboards & reporting.

Conducted customer analytics, product analytics, cohort analysis, segmentation, and retention analysis to drive cross-sell, engagement, and ROI analysis, increasing digital engagement by 18%.

Developed time series analysis, forecasting models, and churn prediction to support decision support analytics for finance and operations teams.

Created interactive Tableau dashboards (Calculated Fields, LODs) and executive reporting views translating complex datasets into actionable KPIs, metrics, and data storytelling for senior leadership.

Delivered ad-hoc reporting, self-service analytics, and KPI tracking & monitoring solutions for risk, compliance, and business stakeholders.

Applied data cleaning, data preparation, data transformation, data validation, reconciliation, and data quality checks to ensure audit- ready datasets aligned with data governance and financial compliance standards.

Partnered with product managers, compliance teams, and cross-functional stakeholders to perform requirements gathering, translate business questions into analytics solutions, and present insights to non-technical stakeholders.

Documented data definitions, metrics logic, testing & validation results, ensuring transparency, version control awareness, and readiness for regulatory reviews and internal audits.

Hexaware Technologies India

Data Analyst Jun 2020 – Apr 2023

Executed data analysis and BI reporting on 200K+ records per engagement using SQL, Advanced SQL, Excel (Pivot Tables, Lookups) and Python, improving reporting accuracy and data reliability by 25%.

Designed, developed, and maintained Power BI dashboards using DAX, Power Query, enabling real-time KPI dashboards, executive reporting, and reducing decision latency by 30%.

Performed EDA, statistical profiling, trend analysis, root cause analysis, and operational analytics to support forecasting, performance monitoring, and problem-solving for enterprise clients.

Built and optimized complex SQL queries across relational databases, improving query performance and scalability by 25%.

Supported ETL validation, data reconciliation, and testing & validation across source systems, data warehouses, and reporting layers during UAT and production releases.

Designed data models (star and snowflake schemas) to improve BI performance, dashboard development, and scalability of reporting platforms.

Prepared clean, high-quality feature datasets for machine learning / AI models, improving downstream model stability and predictive accuracy by 20%.

Worked with Azure Data Factory, Azure SQL, Blob Storage, Synapse (exposure) to support cloud-based analytics workflows and scalable data warehousing solutions.

Delivered weekly, monthly, and ad-hoc reports using Power BI, Excel, SQL, supporting leadership decision-making and business insights generation.

Collaborated in Agile environments with business analysts, QA, ETL teams, and clients, demonstrating strong stakeholder management, communication skills, ownership mindset, and time management.

Ensured adherence to data governance, metadata standards, access controls, and documentation best practices across reporting systems. Hexaware Technologies India

Data Analyst Intern Dec 2019 – May 2020

Collected, cleaned, and prepared raw data using data cleaning, transformation, validation, and reconciliation techniques to ensure accuracy and consistency.

Conducted EDA, statistical analysis, and anomaly detection to support business insights, operational analytics, and decision support.

Wrote SQL queries to extract, aggregate, and validate data from relational databases.

Built interactive dashboards and reports using Power BI and Excel to track KPIs, metrics, and performance indicators.

Assisted with ad-hoc analysis, reporting, documentation, and data quality checks, improving transparency and stakeholder confidence.

Collaborated with cross-functional teams, strengthening analytical thinking, communication, presentation skills, and attention to detail. TECHNICAL SKILLS

Data & Analytics: Data Analysis, Business Intelligence, EDA, Forecasting, Trend Analysis, Variance Analysis, KPI Tracking, Data Storytelling, Decision Support

Programming & Querying: Python, SQL, Advanced SQL, R, SAS Python Libraries: Pandas, NumPy, Matplotlib, Seaborn, SciPy, Scikit-learn, Statsmodels, TensorFlow Visualization & BI Tools: Power BI, DAX, Power Query, Tableau, Looker, LookML, Qlik Sense, Excel, Pivot Tables, VBA Databases & Warehousing: MySQL, PostgreSQL, SQL Server, Oracle, MongoDB, BigQuery, Snowflake, Redshift ETL & Data Integration: SSIS, SSRS, Informatica, Talend, Alteryx, ETL, ELT Cloud Platforms: AWS (S3, Redshift, Lambda), Azure (Data Factory, Synapse), Google Cloud Platform (BigQuery) Big Data Tools: Spark, PySpark, Databricks, Hadoop, Hive Machine Learning & Statistics: Regression, Classification, Clustering, Time Series, Hypothesis Testing, A/B Testing Data Engineering: Data Modeling, Data Wrangling, Data Cleansing, Data Validation, Data Reconciliation Governance & Quality Data Governance, Data Quality, Metadata, Audit Readiness DevOps & Collaboration Git, GitHub, GitLab, CI/CD, Jira, Confluence Methodologies: Agile, Scrum, SDLC

Operating Systems: Windows, Linux, macOS

Soft Skills: Problem Solving, Analytical Thinking, Stakeholder Management, Communication EDUCATION

Robert Morris University, PA, USA

Masters in Information Science / Studies Jun 2023 – Dec 2024 Jawaharlal Nehru Technological University, Hyderabad, India Bachelor of Technology in Computer Science Engineering Jun 2017 – May 2021 PROJECTS

Financial Transaction Fraud Detection System

• Designed and implemented a machine learning–based fraud detection system using Logistic Regression, Random Forest, XGBoost, and Neural Networks on large transactional datasets.

• Performed data cleaning, feature engineering, class imbalance handling (SMOTE), and model evaluation using precision, recall, F1- score, and ROC- AUC.

• Achieved 92%+ recall on fraudulent transactions while reducing false positives through hyperparameter tuning and threshold optimization.

• Visualized risk trends and model outputs using Python (Matplotlib, Seaborn) and presented insights through interactive dashboards. Customer Segmentation & Behavioral Analytics using Machine Learning

• Conducted exploratory data analysis (EDA) and behavioral profiling on customer interaction and transaction datasets to identify spending and engagement patterns.

• Implemented K-Means, Hierarchical Clustering, and PCA for dimensionality reduction and customer segmentation.

• Identified distinct customer segments that enabled targeted marketing and personalization strategies, improving simulated campaign effectiveness by 20%.

• Delivered insights through Power BI dashboards and documented business recommendations for non-technical stakeholders.

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