Data scientist
Sriven Systems Inc.
Richmond, United States of America
2 days ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
SeniorJob location
Richmond, United States of America
Tech stack
Amazon Web Services (AWS)
Business Analytics Applications
Automation of Tests
Azure
Cloud Computing
Continuous Integration
Information Engineering
Data Infrastructure
Monitoring of Systems
Supervisory Control and Data Acquisition (SCADA)
Python
Machine Learning
Power BI
Azure
Unstructured Data
Google Cloud Platform
Feature Engineering
Data Ingestion
Snowflake
Machine Learning Operations
Dataiku
Streamlit Framework
Software Version Control
Databricks
Requirements
- 5+ years of hands-on experience in Data Science using Python and/or R, with a strong focus on analyzing large, complex, and high-volume datasets
- Proven ability to translate complex analytical findings into clear, actionable insights for business leaders, engineers, operations teams, and executives
- Experience designing, developing, and deploying advanced analytics and machine learning solutions aligned to business objectives
- Ability to create clear, interpretable visualizations that tell a compelling story, support decision-making, and align with executive-level messaging
- Demonstrated experience creating interactive dashboards, reports, and applications (e.g., RShiny, Power BI, Streamlit, Dash) for business consumption
- Strong experience working with structured, semi-structured, and unstructured data (e.g., sensor/SCADA data, time-series data, text, images)
- Expertise across machine learning, statistical modeling, forecasting, optimization, and anomaly detection, with real-world application experience
- Experience or working knowledge of MLOps practices including model development lifecycle management, automated testing, CI/CD pipelines, version control, and deployment (e.g., MLflow, Dataiku, Azure ML, or similar tools)
- Strong understanding of model monitoring, including performance tracking, explainability, bias detection, model drift, and reproducibility in production environments
- Experience or working knowledge of data engineering concepts, including data ingestion, transformation, feature engineering, and data quality controls
- Experience with cloud and modern analytics platforms (AWS, Azure, Google Cloud Platform, Snowflake, Databricks, or similar) is a strong plus
- Understanding of governance, security, and regulatory requirements for enterprise and utility data environments is preferred
