Senior Data Science Analyst
Ampcus
Required Emphasis on:
- Experience designing, developing, and deploying advanced analytics and machine learning solutions aligned to business objectives
- Expertise across machine learning, statistical modeling, forecasting, optimization, and anomaly detection, with real world application experience
- Develop end to end data science solutions, from data acquisition and feature engineering to model deployment and post production monitoring
- MUST have 5 years of experience in Data Science using Python or R, with a strong focus on analyzing large, complex, and high-volume datasets
Required Skills and Experience
- MUST have prior hands on experience as a Data Scientist on a project using Python or R
- Proven ability to translate complex analytical findings into clear, actionable insights for business leaders, engineers, operations teams, and executives
- 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, image
- 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
- 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, GCP, Snowflake, Databricks, or similar) is a strong plus
- Understanding of governance, security, and regulatory requirements for enterprise and utility data environments is preferred
What soft skill requirements do you have (team fit and personality requirements)?
- Strong communication skills both verbal and written
- Ability to lead, collaborate, or work effectively in a variety of teams, including multi-disciplinary teams
Nice to Have Skills:
- Understanding and/or Experience with data engineering is a plus
- Experience with cloud technologies(AWS, Azure, GCP, Snowflake) is big plus
High Level Project Overview:
This role serves as a technical consultant and senior individual contributor within ***'s Enterprise Data Analytics team, delivering advanced analytics and data science solutions that support operational reliability, grid modernization, customer experience, and clean energy initiatives.
Key responsibilities include:
- Partner with business units such as Generation, Transmission & Distribution, Grid Operations, Asset Management, Customer Operations, and Finance to identify high value data science use cases
- Design, build, and deploy predictive, prescriptive, and diagnostic models to support:
- Asset health and predictive maintenance
- Load forecasting and demand modeling
- Outage prediction, restoration optimization, and reliability analytics
- Grid resilience, renewable integration, and emissions reduction initiatives
- Customer behavior, billing, and energy efficiency programs
- Apply advanced techniques such as time series forecasting, survival analysis, optimization, clustering, NLP, and anomaly detection to utility scale data
- Develop end to end data science solutions, from data acquisition and feature engineering to model deployment and post production monitoring
- Support implementation of MLOps best practices to ensure scalable, reliable, and auditable analytics solutions in compliance with enterprise and regulatory standards
- Collaborate closely with data engineers, platform teams, and cloud architects to ensure models are production ready and performant
- Build reusable analytical frameworks and accelerators that improve time to value across the Enterprise Analytics portfolio
- Create intuitive visualizations, dashboards, and self-service analytics tools that empower stakeholders to explore insights independently
- Mentor junior data scientists and analysts, contributing to analytics standards, code quality, and best practices
- Support ***'s commitment to safety, reliability, affordability, and clean energy transformation through responsible and ethical use of data and AI
Required Years of Experience:
- MUST have 5 years of experience in Data Science using Python or R, with a strong focus on analyzing large, complex, and high-volume datasets
Education:
- Education: Bachelors or higher required
- Discipline: Computer Science, Information Systems, Mathematics
Are there any specific companies/industries you'd like to see in the candidate's experience?
- High Preference for candidates that have previously worked with a large scale commercial utilities team but will review candidates who have a background with large scale capital projects for companies
Preferred Interview Process Overview (High level):
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