Data Scientist

General Responsibilities • Design and implement data pipelines, analytics models, and machine-learning solutions to support process automation and predictive decision-making. • Collect, clean, and validate structured and unstructured data from multiple enterprise systems, ensuring data quality and integrity. • Perform statistical analysis, forecasting, and pattern recognition to identify process improvement opportunities. • Develop interactive dashboards and visualizations using tools like Power BI or Tableau for real-time performance tracking. • Collaborate with Solution Architects and Business Analysts to integrate data insights into workflow automation and digital solutions. • Support the creation and enforcement of data governance, metadata, and access-control frameworks in coordination with cybersecurity policies. • Evaluate and deploy AI/ML models (e.g., classification, regression, clustering, NLP) within automation and decision-support platforms. • Implement ETL processes and maintain scalable data storage on cloud platforms such as AWS, Azure, or GCP. • Provide technical documentation, data dictionaries, and model explainability reports for audit and compliance purposes. • Support training and knowledge transfer, enabling end users to interpret and leverage analytical insights. Minimum Qualifications • Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Engineering, or related discipline. • Experience: • 5–8 years of experience in data analytics, machine learning, or AI model deployment. • Proven success designing analytical solutions for IT modernization, automation, or enterprise transformation projects. • Experience working with large, complex datasets and applying data-driven methods to improve business or operational performance. Technical Expertise • Programming & Analysis: Python, R, SQL, Power Query, or PySpark. • Machine Learning & AI: scikit-learn, TensorFlow, Keras, or PyTorch. • Data Visualization: Power BI, Tableau, Matplotlib, or Plotly. • Data Engineering: ETL tools, APIs, REST services, and cloud data storage (AWS S3, Azure Data Lake, GCP BigQuery). • Statistics & Modeling: Regression, time-series forecasting, clustering, and anomaly detection. • Governance & Security: Familiarity with NIST data-handling standards, FedRAMP compliance, and secure data pipelines. Preferred Certifications • Microsoft Certified: Data Scientist Associate or Azure AI Engineer • Google Professional Data Engineer or AWS Certified Data Analytics – Specialty • Certified Analytics Professional (CAP) – INFORMS • Lean Six Sigma Green Belt (for data-driven process improvement) • Power BI Data Analyst Associate