Senior Data Scientist & Modeler

Iberdrola•Orange, CT

1d•Onsite

About The Position

The Senior Data Scientist & Modeler position is responsible for designing and maintaining the data structures that power business applications, analytics platforms, and emerging AI capabilities. This role ensures that data is well-organized, high-quality, and ready for use in reporting, machine learning models, and automation. The Senior Data Scientist & Modeler role is also responsible for developing ontologies, knowledge graphs, rules or other symbolic models, or insights into specific business processes or domains. This role designs, builds and maintains systems that manage and utilize knowledge efficiently, often within the context of AI or ML. The Senior Data Scientist & Modeler role reports into the Senior Manager, Data & AI Transformation.

Requirements

Bachelor’s degree in Computer Science, Data Science, Software Engineering, Information Systems or a related field and a minimum of five (5) years of relevant experience. An equivalent combination of education and experience may be considered.
Minimum five (5) years of experience in data modeling, database design, or related field.
Experience with relational, dimensional, and analytical modeling; ETL; and modern data platforms (RDBMS, NoSQL).
Demonstrated experience integrating and managing large volumes of data across diverse sources is required
Demonstrated experience working with knowledge management solutions
Strong knowledge of data modeling concepts and best practices.
Proficiency with data modeling tools such as Erwin or ER/Studio.
Advanced understanding of information models, knowledge representation and reasoning
Proficiency in semantic technologies (e.g., RDF, OWL, SPARQL) and knowledge graph development
Strong SQL skills; familiarity with Python, R, or similar languages.
Understanding relational, dimensional, and data warehouse modeling.
Strong communication and documentation skills.
Exposure to AI/ML concepts, vector databases, feature engineering, or knowledge graphs is a plus
Proficiency in the design and implementation of modern data architectures and concepts such as cloud services (AWS, Azure, GCP) and modern data warehouse tools (Snowflake, Databricks)
Ability to collaborate within and across teams of different technical knowledge to support delivery and educate end users on data products
Excellent business acumen and interpersonal skills; able to work across business lines at a senior level to influence and effect change to achieve common goals.
Ability to describe business use cases/outcomes, data sources and management concepts, and analytical approaches/options
Ability to translate among the languages used by executive, business, IT, and quant stakeholders.

Nice To Haves

Master’s degree in Computer Science, Data Science, Software Engineering, Information Systems or a related field
Prior experience preparing data for machine learning or analytics environments
Prior experience with semantic web technologies and linked data principles

Responsibilities

Designs and maintains conceptual, logical, and physical data models.
Builds and operationalizes data solutions including transformations, cleansing, and quality improvements.
Translates business requirements into scalable data models supporting analytics and AI use cases.
Creates and implements data models and ontologies to support knowledge representation by organizing large volumes of data into knowledge structures
Develops and maintains documentation for knowledge graphs as well as knowledge engineering processes and systems
Tests and evaluates new models to identify areas of improvement and optimization
Analyzes existing information models and standards and identifies their advantages and disadvantages
Creates ERDs, data flow diagrams, and other model documentation.
Collaborates with data engineers, AI engineers, and DBAs to implement models across databases, warehouses, and data lakes.
Applies data modeling best practices and resolve data consistency issues.
Optimizes models for performance, security, and reliability.
Incorporates data governance, security, and quality standards.
Prepares data structures for ML pipelines, feature stores, and LLM-ready datasets.
Supports AI teams with data models for RAG pipelines and semantic/knowledge graph architecture.
Collaborates with data scientists and analysts to optimize models and algorithms for data quality, security, and governance
Integrates data from different sources, including databases, data warehouses, APIs, and external systems
Ensures data consistency and integrity during the integration process, performing data validation and cleaning as needed
Optimizes data pipelines and data processing workflows for performance, scalability, and efficiency
Implements data quality checks and validations within data pipelines to ensure the accuracy, consistency, and completeness of data
Takes authority, responsibility, and accountability for exploiting the value of enterprise information assets and of the analytics used to render insights for decision making automated decisions and augmentation of human performance
Works with board members and other executives to establish the vision for managing data as a business asset
Establishes the governance of data and algorithms used for analysis, analytical applications, and automated decision making.