- Location
- USA (Westport, CT), United States of America
- Workplace
- Remote
- Type
- Full-time
- Department
- Education
- Seniority
- Senior
- Source
- Workday
Description
Dynata is seeking a Senior Lead Data Scientist for Graph & Forecasting to lead the development of predictive intelligence capabilities that power key operational, commercial, and product decisions across the organization.
This role sits at the intersection of graph analytics, identity resolution, and advanced forecasting. The Senior Lead Data Scientist will be responsible for designing and optimizing graph-based representations of Dynata's data assets, developing predictive models that improve key business outcomes, and ensuring analytical models are robust, scalable, and production-ready.
Reporting to the VP, Data Science, this individual will partner closely with product, engineering, and platform teams to build capabilities supporting use cases such as identity resolution, feasibility prediction, panel health monitoring, audience intelligence, dynamic pricing, and operational optimization.
Key Responsibilities
Graph Science & Identity Intelligence
Architect and optimize graph-based data models supporting audience intelligence, project similarity analysis, clustering, and relationship-driven analytics.
Develop and maintain resilient identity resolution frameworks across multiple respondent identifier systems using deterministic and probabilistic matching techniques.
Design graph structures that support downstream analytics, forecasting, optimization, and AI applications.
Continuously evaluate graph performance, scalability, and business impact.
Forecasting & Predictive Modeling
Build and maintain forecasting models for supply prediction, incidence estimation, completion probability, panel health, and other business-critical use cases.
Develop time-series and predictive models that account for changing respondent behavior, market dynamics, and operational conditions.
Extend forecasting approaches to support scenario analysis, optimization, and decision support.
Monitor model performance and identify opportunities for continuous improvement.
Model Quality & Technical Leadership
Design and execute rigorous validation strategies to assess model accuracy, stability, scalability, and operational readiness.
Establish best practices for model evaluation, experimentation, monitoring, and governance.
Serve as a technical authority for graph analytics, forecasting methodologies, and production-grade machine learning.
Ensure solutions are designed for long-term maintainability, performance, and business value.
Cross-Functional Collaboration
Partner closely with Product, Technology, and Data Platform teams.
Collaborate on schema design, feature engineering strategies, and data contracts to ensure platform capabilities support analytical requirements.
Translate complex analytical findings into actionable business recommendations.
Influence technical and business stakeholders on analytical investments, priorities, and roadmap decisions.
Qualifications
8+ years of hands-on experience in data science, applied machine learning, analytics, or related fields.
Proven experience developing and deploying graph analytics, machine learning, or predictive modeling solutions in production environments.
Deep expertise in graph analytics, including graph databases, graph algorithms, similarity modeling, clustering, and network analysis.
Strong experience with graph technologies such as Neptune, Neo4j, TigerGraph, or comparable platforms.
Strong background in forecasting, time-series analysis, statistical modeling, and predictive analytics.
Advanced proficiency in Python and modern data science tooling.
Experience working with large-scale, noisy, real-world operational datasets.
Demonstrated ability to make complex technical decisions and operate effectively in ambiguous problem spaces.
Strong communication skills with the ability to explain complex analytical concepts to technical and non-technical stakeholders.
Experience collaborating with product, engineering, and platform teams to deliver production-ready solutions.
Preferred Qualifications
Experience with identity resolution, entity resolution, master data management, or identity graph development.
Experience applying graph analytics to similarity modeling, community detection, clustering, relationship discovery, recommendation, and graph embeddings.
Experience with forecasting, trend and seasonality detection, anomaly and change-point detection, cohort evolution, longitudinal measurement, and demand planning,.
Experience in market research, panel data, audience measurement, advertising technology, marketplaces, or related industries.
Experience with cloud-native analytics and machine learning environments, including AWS ecosystem.
Familiarity with optimization, simulation, or decision-support systems.
At Dynata, we deliver the highest quality first-party data to help businesses around the world gain precise insights, activate the right audiences, and confidently measure impact. With industry-leading respondent accuracy, reliability, and a commitment to continuous improvement, Dynata is the trusted foundation for smarter decision-making.
At Dynata, we are committed to creating an inclusive and accessible environment where every employee and customer feels valued, respected, and supported. We strive to build a workforce that reflects the diversity of the communities we serve. Dynata welcomes and encourages applications from individuals with disabilities and is dedicated to fostering a work culture that supports everyone. Accommodations are available upon request for all aspects of the selection process.
Dynata is an Equal Opportunity Employer. We consider all qualified applicants and employees without regard to race, color, religion, sex (including pregnancy, sexual orientation, and gender identity), national origin, marital status, age, disability, genetic information, veteran status, or any other legally protected status under applicable laws.
The base salary range for this position in is $120K-$155K/yr; however, base pay offered may vary depending on location, job-related knowledge, skills, and experience. A discretionary incentive program may be provided as part of the compensation package, in addition to a full range of medical and other benefits, dependent on full-time employment status.
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