Job Specifications
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