Data Science Specialist

Mandatory skill Python + ML (Machine Learning) Data Science

Experience: 5-12 Years only

Results-driven Data Scientist with hands-on experience in data analysis, data wrangling, and business insights generation using Python and SQL. Adept at translating complex datasets into actionable recommendations. Possesses a solid foundation in statistics and machine learning, with emerging exposure to large language models (LLMs) and OpenAI technologies.

Key Skill Areas:

Programming & Tools: Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn), SQL (complex queries, joins, CTEs, window functions), Git

Data Analysis & Visualization: Exploratory Data Analysis (EDA), trend and correlation analysis, hypothesis testing, dashboards (Power BI/Tableau), storytelling with data

Databases & Data Handling: SQL Server, MySQL, basic knowledge of Spark/BigQuery/Hive is a plus

Statistics & ML (Foundational): Descriptive and inferential statistics, regression, classification, clustering (K-Means), model evaluation metrics (accuracy, precision, recall, AUC), overfitting/underfitting, cross-validation

LLM/AI Exposure: Familiarity with prompt engineering, OpenAI APIs, basic usage of GPT models for data/text automation, awareness of LLM limitations and applications in analytics

Soft Skills: Strong problem-solving ability, attention to detail, stakeholder communication, ability to translate business problems into data solutions

Experience Highlights:

Conducted in-depth data analysis to uncover trends, patterns, and anomalies that informed strategic decisions across product, marketing, and operations teams

Designed and implemented scalable data pipelines and automated data workflows using Python and SQL

Developed and maintained analytical models and dashboards to track key business metrics and performance indicators

Applied statistical methods and machine learning techniques to solve real-world business problems such as forecasting, segmentation, and performance optimization

Collaborated with stakeholders to gather requirements, translate business questions into analytical approaches, and communicate findings with clarity

Explored the use of LLMs (e.g., OpenAI GPT) for enhancing internal workflows and accelerating data-driven tasks such as querying, summarization, and content generation