Young Management & Consulting (YMC) is seeking an experienced Data Scientist to support a major utility organization in the development and deployment of advanced analytics, predictive modeling, Machine Learning (ML), and Artificial Intelligence (AI) solutions.
This position will transform large and complex datasets into actionable business intelligence that supports strategic decision-making across customer strategy, marketing, product sales, operations, and other business functions. The Data Scientist will be responsible for the full analytical lifecycle—from data acquisition, preparation, and exploratory analysis through model development, validation, deployment, monitoring, and continuous improvement.
The successful candidate will combine strong statistical and analytical expertise with advanced programming, machine learning, cloud technologies, and business acumen. This individual must also have the ability to translate sophisticated technical concepts into clear business recommendations for stakeholders at all levels of the organization.
This role will also serve as a technical resource and mentor to junior Data Scientists and analysts while helping establish scalable frameworks and best practices for Machine Learning and AI across the organization.
Key ResponsibilitiesMachine Learning, AI & Predictive Modeling
- Design, develop, test, enhance, and deploy advanced statistical and predictive models using Machine Learning and Artificial Intelligence techniques.
- Apply both supervised and unsupervised learning techniques to solve complex business problems.
- Develop scalable frameworks and standards for Machine Learning and AI model development and deployment.
- Evaluate, validate, and continuously monitor model performance, accuracy, stability, and business effectiveness.
- Identify model degradation or changes in underlying data and recommend appropriate model updates or retraining.
- Perform feature engineering, feature extraction, variable selection, model fitting, segmentation, classification, clustering, and other advanced analytical techniques.
- Apply data mining, text mining, statistical modeling, and decision-support methodologies where appropriate.
- Support the deployment of production-level analytical and Machine Learning solutions within cloud environments.
- Train and mentor other team members on Machine Learning, AI, statistical modeling, and analytical frameworks.
Advanced Analytics & Business Strategy
- Leverage quantitative research and advanced analytics to identify trends, relationships, patterns, risks, and business opportunities.
- Combine and analyze information from customer, sales, marketing, operational, financial, geographic, and external data sources.
- Evaluate market conditions, customer behavior, internal performance metrics, and competitive information to identify strategic opportunities.
- Develop analytical approaches that support growth strategies, customer engagement, marketing effectiveness, and product performance.
- Analyze program and campaign results and provide recommendations for future strategies and initiatives.
- Identify customer trends and behaviors to determine the potential impact and success of proposed programs and strategies.
- Analyze sales and customer participation data to monitor performance and identify opportunities for improvement.
- Translate complex analytical findings into meaningful recommendations for executives, business leaders, and non-technical stakeholders.
Data Engineering, ETL & Big Data
- Work with large-scale structured and unstructured datasets across multiple enterprise systems.
- Design, develop, and maintain ETL processes supporting analytics, Machine Learning, reporting, and business intelligence solutions.
- Extract, transform, integrate, and prepare data from multiple enterprise databases and technology platforms.
- Work with big-data and cloud technologies, including Azure, AWS, Hadoop, Spark, or comparable platforms.
- Develop solutions utilizing technologies including SQL, Python, R, SAS, Azure, Power BI, and Excel.
- Independently query large enterprise databases and prepare data for statistical analysis and model development.
- Support data migration and integration across datasets, applications, and technology platforms.
- Perform exploratory data analysis and apply appropriate data-reduction techniques to large and complex datasets.
- Debug, monitor, and troubleshoot data pipelines, models, and analytical solutions.
Business Intelligence & Data Visualization
- Design and develop dashboards, reports, data visualizations, and business intelligence solutions.
- Utilize tools such as Power BI, Tableau, SSRS, and other visualization platforms to communicate complex findings.
- Create effective presentation-layer solutions that allow stakeholders to easily understand analytical results.
- Develop reporting and visualization tools that support both technical and non-technical audiences.
- Identify key information sources, assess data quality, and communicate limitations associated with available data.
Data Quality & Governance
- Identify data anomalies, inconsistencies, and quality issues across enterprise data sources.
- Partner with business and technical teams to conduct root-cause analysis and resolve data-related issues.
- Develop and maintain high-quality analytical documentation, including:
- Data dictionaries
- Metadata
- Model documentation
- Data-pull instructions
- Business rules
- Technical specifications
- Data lineage and source documentation
- Improve organizational access to and understanding of internal and external data sources.
- Establish repeatable analytical methodologies and processes that improve data quality, consistency, and reliability.
Leadership & Business Partnership
- Serve as a technical subject-matter expert for advanced analytics, Machine Learning, and AI initiatives.
- Provide technical guidance and mentorship to junior Data Scientists and analysts.
- Work directly with business leaders to identify opportunities where analytics, AI, and Machine Learning can improve decision-making.
- Translate business challenges and requirements into technical analytical solutions.
- Communicate complex technical concepts in clear business language to stakeholders at varying levels of technical expertise.
- Build productive relationships across business, technology, marketing, finance, operations, and analytics teams.
- Influence data-driven decision-making across the organization through strong analytical consulting and communication.
- Manage multiple concurrent analytical projects with competing priorities and deadlines.
Required QualificationsEducation
- Bachelor’s degree in Business Analytics, Statistics, Applied Mathematics, Management Information Systems (MIS), Computer Science, Data Science, or a related quantitative or analytical discipline.
Experience
- Minimum 3–5 years of professional experience in Data Science, advanced analytics, predictive analytics, business analytics, marketing analytics, spatial analytics, or a related field.
- Demonstrated experience developing advanced statistical and predictive models.
- Experience applying supervised and unsupervised Machine Learning techniques.
- Strong experience manipulating and analyzing large datasets and enterprise databases.
- Experience independently querying, extracting, transforming, and preparing data for advanced analysis.
- Experience developing or supporting Machine Learning and/or Artificial Intelligence solutions.
- Experience working with cloud or big-data technologies such as Microsoft Azure, AWS, Hadoop, or Spark.
- Experience with data visualization and business intelligence platforms such as Power BI, Tableau, or SSRS.
Technical Skills
Candidates should demonstrate strong technical competency in several of the following areas:
- Python, R, and/or SAS
- SQL, T-SQL and/or PL/SQL
- Machine Learning
- Artificial Intelligence
- Predictive Modeling
- Statistical Modeling
- Supervised & Unsupervised Learning
- Feature Engineering & Variable Selection
- Model Validation & Performance Monitoring
- Azure and/or AWS
- Hadoop / Spark
- ETL Development
- Enterprise Databases
- Power BI
- Tableau
- SSRS
- GIS / Spatial Analytics
- Advanced Microsoft Excel
- Data Mining & Exploratory Data Analysis
- Data Visualization
- Data Integration & Migration
- Basic HTML knowledge
Preferred Qualifications
- Master’s degree, MBA, Ph.D., or progress toward an advanced degree in Business Analytics, Statistics, Applied Mathematics, Computer Science, Data Science, Machine Learning, Artificial Intelligence, or a related field.
- Experience deploying production-level Machine Learning or AI models in cloud environments.
- Experience developing and implementing Machine Learning/AI frameworks or model governance standards.
- Experience with marketing analytics, database marketing, customer segmentation, and campaign strategy.
- Experience coding, testing, and implementing campaign data initiatives using real-time decision-making technology.
- Experience with Geographic Information Systems (GIS) and spatial analysis.
- Experience working with financial statements and financial data.
- Knowledge of or previous experience within the utility, energy, engineering, or infrastructure industries.
- Experience providing technical leadership or mentoring junior Data Scientists and analysts.
Core Competencies
- Advanced analytical and quantitative reasoning
- Strong programming and technical problem-solving ability
- Ability to transform complex technical concepts into actionable business insights
- Strong consulting and stakeholder-management skills
- Excellent verbal, written, and presentation skills
- Strong project-management and organizational capabilities
- Ability to manage multiple concurrent assignments and conflicting priorities
- Ability to independently investigate and solve complex analytical problems
- Strong attention to data quality and analytical accuracy
- Self-motivated with the ability to work independently with minimal guidance
- Continuous learner with an understanding of emerging trends in Data Science, AI, and Machine Learning
- Strong collaboration and teamwork
- Ability to hold oneself and others accountable for results
- Comfortable working in a fast-paced, deadline-driven, and evolving environment
Ideal Candidate
The ideal candidate is a Data Scientist who can bridge the gap between advanced technology and real-world business decisions. This individual should be equally comfortable developing a Machine Learning model in Python, querying millions of records with SQL, working within a cloud data environment, building a Power BI visualization, and presenting the resulting recommendation to business leadership.
We are looking for someone who doesn't simply build models—they understand why the model matters, how it should influence a business decision, and how to communicate that insight to the people responsible for acting on it.
The strongest candidate will have the technical depth to work independently across the full data science lifecycle while also possessing the business judgment and communication skills to serve as a trusted analytical partner. They should be naturally curious, comfortable questioning assumptions, and capable of identifying opportunities within the data that may not have been part of the original request.
Experience within utilities, energy, marketing analytics, customer analytics, or GIS/spatial analytics is highly desirable. The candidate should also be capable of providing technical leadership and mentorship to junior team members while helping the organization expand its use of Machine Learning, Artificial Intelligence, predictive analytics, and data-driven decision-making.
Job Type: Full-time
Pay: From $95,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Tuition reimbursement
- Vision insurance
Experience:
- Public utility: 5 years (Required)
- Python: 5 years (Required)
- SQL: 5 years (Required)
- Power BI: 5 years (Preferred)
Work Location: In person
