Data Scientist – Analytics

Candidate – Data Scientist – Analytics & Insights Summary

Analytical and detail-oriented Data Scientist with hands-on experience in analytics, machine learning, and AI-driven solutions across data-intensive projects. Demonstrated ability to design and deliver end-to-end analytical workflows that support business, product, and engineering teams in making informed decisions. Strong expertise in data exploration, statistical analysis, feature engineering, and predictive modeling based on well-defined business requirements and user stories. Actively engaged in Agile practices including sprint planning, daily stand-ups, reviews, and retrospectives.

Experienced in building dashboards, validating data quality, and evaluating model performance to ensure reliable outcomes. Known for effective communication, structured problem-solving, and close collaboration with cross-functional stakeholders. Committed to applying best practices in data science and analytics to deliver scalable, high-quality insights under tight timelines.

• Conducted data analysis and exploratory analysis using Python and SQL to support business and product reporting needs.
• Supported machine learning initiatives by preparing clean, structured datasets from raw operational data.
• Assisted in developing and maintaining data pipelines to extract, transform, and load data into Snowflake.
• Performed data cleaning, validation, and consistency checks to ensure accuracy and reliability of analytics outputs.
• Analyzed transactional, order, and log data to identify trends, usage patterns, and system behavior.
• Supported the definition and tracking of KPIs and performance metrics used by product and engineering teams.
• Developed and maintained Power BI and Tableau dashboards for recurring business reviews and reporting.
• Assisted with feature preparation and basic feature engineering for predictive modeling efforts.
• Built and supported ETL/ELT data pipelines to ingest and transform operational data into Snowflake.
• Implemented data validation and quality checks within pipelines to ensure reliable downstream analytics.
• Automated recurring data extraction and transformation workflows using Python and SQL.
• Collaborated with engineering teams to understand data sources and resolve data-related issues.
• Helped identify data gaps and anomalies during analysis and reporting activities.
• Contributed to documentation for datasets, queries, and reporting logic to support team knowledge sharing.
• Participated in cross-functional discussions to translate business questions into data and analytics tasks.
• Supported ad-hoc data analysis requests from stakeholders as needed.

Analytics & Data Science: Exploratory Data Analysis (EDA), Statistical Analysis, Feature Engineering, Predictive Modeling, Forecasting, Classification, Clustering, Anomaly Detection, Root Cause Analysis, KPI Definition & Tracking, Data Quality Validation, Business Insights, Documentation & Reporting

Machine Learning & AI: Supervised & Unsupervised Learning, Regression, Classification, Time Series Models (ARIMA, SARIMA, Holt-Winters), Deep Learning, Generative AI, Model Evaluation, Hyperparameter Tuning, MLflow

NLP & Generative AI: GPT-4, Gemini, LLaMA, BERT, Lang Chain, RAG Pipelines, Hugging Face Transformers, FAISS, Sentiment Analysis, Semantic Search, Prompt Engineering

Visualization & BI: Power BI, Tableau, Streamlit, Dash

Methodologies & Concepts: SDLC, Agile (Scrum), Data Lifecycle, Experimentation, A/B Testing, Business Requirement Analysis

• AWS Cloud Practitioner:
  Certified in foundational AWS cloud concepts including core services, security, pricing, and cloud architecture.
• Databricks with Generative AI:
  Certified in applying Databricks capabilities for generative AI use cases, including model training, deployment, and data pipeline integration.
• Databricks Fundamentals:
  Certified in Databricks core functionalities, covering data management, collaborative workflows, and analytics.
• Generative AI Fundamentals:
  Certified with knowledge of core generative AI concepts, applications, and responsible AI practices.
• GenAI Tools & AI Agents for Software Testing:
  Certified in applying GenAI tools and AI-driven agents to enhance efficiency and coverage in software testing processes.
• Generative AI Application Development:
  Certified in developing, integrating, and deploying applications using generative AI frameworks and tools.

University of North Texas – Denton, Texas

Master’s in Data Analytics – Dec 2024