Data Scientist – Analytics – Simarn Solutions

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.

SKILLS

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, LangChain, RAG Pipelines, Hugging Face Transformers, FAISS, Sentiment Analysis, Semantic Search, Prompt Engineering

Programming & Data Engineering:
Python, SQL, PySpark, Pandas, NumPy, ETL Pipelines, Automation, Git, GitHub

Cloud & Platforms:
AWS, Google Cloud Platform (Vertex AI), Snowflake, Docker, CI/CD Concepts

Databases:
Snowflake, MySQL, PostgreSQL, MongoDB, Cassandra, ElasticSearch

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

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

Client – (Distribution) | Dallas, Texas

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.
CERTIFICATIONS
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.