Sr. Data Analyst

Job Title

Designing and maintaining data systems and databases; this includes fixing coding errors and other data-related problems. Mining data from primary and secondary sources, then reorganizing said data in a format that can be easily read by either human or machine. Using statistical tools to interpret data sets, paying particular attention to trends and patterns that could be valuable for diagnostic and predictive analytics efforts.

Required Skills/Experience

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or related field. • Strong experience in machine learning algorithms, predictive modeling, and data mining. • Proficiency in Python (required) for data science workloads. • Strong SQL (required) knowledge and experience with relational databases. • Proficiency in PySpark(required) for data science workloads. • Minimum 3 years of experience with data visualization tools such as Power BI, Dax Queries, and best practices. • Experience with Azure Databricks, Oracle, and modern data science libraries (e.g., scikit-learn, pandas, NumPy). • Experience with GenAI and large language models. • Ability to interpret complex datasets and produce actionable insights. • Must know how to analyze the root cause of dashboard errors. • Have experience in ML Ops and have strong coding background. • Have experience with Natural Language Processing (NLP). • Knowledge or experience with A/B Testing. • Working knowledge of designing, training, and implementing machine learning models. • Familiarity with cloud-based infrastructure • Excellent communication and problem-solving skills. • 7 or more years of experience in data science and machine learning engineering.

Additional Skills

• Knowledge of statistical methods and experimental design.

Key Responsibilities

Advanced Analytics & Machine Learning o Design, develop, and optimize machine learning models (forecasting, classification, clustering). o Apply data mining techniques to uncover patterns and insights in large datasets. o Perform feature engineering, model validation, and performance tuning. o Explore and deploy modern AI and ML approaches to enhance automation and analytics. Data Preparation & Quality o Prepare structured and unstructured data for modeling and advanced analysis. o Develop scripts and tools for data cleansing, validation, and enrichment. o Collaborate with Data Engineering to maintain efficient data pipelines. o Identify data quality issues and propose remediation. Analytics, Insights & Reporting o Conduct deep-dive analyses to identify trends and improvement opportunities. o Communicate complex findings in clear, concise ways to technical and non-technical stakeholders. o Support the development of dashboards, metrics, and analytical solutions. Cross-Team Collaboration o Work with architects, engineers, and analysts to define analytical requirements. o Contribute to conceptual data model design and workflow optimization. o Promote best practices in machine learning, analytics, and data governance.