Dynata

Senior Lead Data Scientist, Graph & Forecasting

Dynata • $120K — $155K *
US-AnywhereRemote in United States
Information Technology
8 - 10 years of experience
Today
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Job Overview by Ladders

Qualifications

  • 8+ years of experience in data science or related fields.
  • Experience with graph analytics, machine learning, or predictive modeling in production environments.
  • Expertise in graph technologies like Neptune or Neo4j.
  • Strong background in forecasting and statistical modeling.
  • Proficiency in Python and data science tooling.
  • Experience with large operational datasets.
  • Excellent communication skills for technical and non-technical audiences.

Responsibilities

  • Architect and optimize graph-based data models for audience intelligence and analytics.
  • Develop identity resolution frameworks using matching techniques.
  • Design graph structures for downstream analytics and AI applications.
  • Build forecasting models for key business use cases.
  • Extend predictive models for scenario analysis and optimization.
  • Conduct validation to ensure model accuracy and scalability.
  • Collaborate with cross-functional teams on data strategies and decision support.

Benefits

  • Full range of medical and other benefits available for full-time employees.
  • Discretionary incentive program included in compensation package.
Full Job Description
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.


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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About Dynata

Dynata is a global data and survey research company that provides data-driven insights to help businesses make informed decisions. The company offers a range of services, including market research, polling, and data analytics. Dynata has a presence in over 100 countries and serves clients in a variety of industries, including healthcare, finance, and technology. The company was founded in 2018 and is headquartered in Plano, Texas.
Learn more about Dynata
Size
1,500 employees
Industry
Media
* Ladders Estimates

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