Data Science Specialist
LTM
Job Description
Mandatory skill - Python + ML (Machine Learning) - Data ScienceExperience: 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), GitData Analysis & Visualization: Exploratory Data Analysis (EDA), trend and correlation analysis, hypothesis testing, dashboards (Power BI/Tableau), storytelling with dataDatabases & Data Handling: SQL Server, MySQL, basic knowledge of Spark/BigQuery/Hive is a plusStatistics & ML (Foundational): Descriptive and inferential statistics, regression, classification, clustering (K-Means), model evaluation metrics (accuracy, precision, recall, AUC), overfitting/underfitting, cross-validationLLM/AI Exposure: Familiarity with prompt engineering, OpenAI APIs, basic usage of GPT models for data/text automation, awareness of LLM limitations and applications in analyticsSoft 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 teamsDesigned and implemented scalable data pipelines and automated data workflows using Python and SQLDeveloped and maintained analytical models and dashboards to track key business metrics and performance indicatorsApplied statistical methods and machine learning techniques to solve real-world business problems such as forecasting, segmentation, and performance optimizationCollaborated with stakeholders to gather requirements, translate business questions into analytical approaches, and communicate findings with clarityExplored the use of LLMs (e.g., OpenAI GPT) for enhancing internal workflows and accelerating data-driven tasks such as querying, summarization, and content generation