Principal Data Scientist - Advanced Analytics

Vericast•San Antonio, TX

4d•$170,000 - $200,000

About The Position

Vericast is investing in the next generation of Advanced Analytics, Experimentation, and Measurement, and we're looking for a Principal Data Scientist to help lead the way. This is a highly visible opportunity for a data science leader who thrives at the intersection of experimentation, causal inference, machine learning, and Generative AI. In this role, you'll shape our analytics discipline from the ground up, partnering with executive leaders and cross-functional teams to solve complex business challenges with data. You'll design rigorous experimentation frameworks, build and deploy scalable AI and machine learning solutions, and transform complex insights into clear recommendations that drive growth and measurable business impact. This is a senior individual contributor role that offers the opportunity to influence strategy, establish best practices, mentor other data scientists, and leave a lasting mark on how Vericast leverages data to make decisions.

Requirements

Master's degree in Statistics, Data Science, Computer Science, Economics, Mathematics, Engineering, or a related quantitative field required.
8+ years of progressive experience in data science, analytics, or machine learning roles with significant hands-on experience working with large-scale data environments.
Experience within financial services or another highly regulated, data-intensive industry preferred.
Proven success leading or serving as a senior technical contributor on experimentation, measurement, and advanced analytics initiatives.
Demonstrated ability to translate complex business challenges into scalable analytical solutions that drive measurable business outcomes.
Experience influencing senior stakeholders and partnering with cross-functional teams to deliver data-driven insights and recommendations.
Deep expertise in marketing experimentation, including A/B testing, multivariate testing, experimental design, metric development, and lift/incrementality measurement.
Strong background in causal inference methodologies, hypothesis testing, statistical modeling, and power analysis.
Proven ability to design, execute, validate, and scale experimentation programs that enable confident business decision-making.
Experience establishing governance, best practices, and standards for experimentation across an organization.
Hands-on experience developing and deploying Market Mix Models (MMM), Attribution Models, and Uplift Models.
Strong experience working with Machine Learning, Large Language Models (LLMs), Generative AI solutions, and emerging agent-based frameworks such as LangChain, LangSmith, or similar technologies.
Expertise in machine learning techniques including clustering, classification, regression, neural networks, anomaly detection, recommendation engines, and predictive modeling.
Advanced programming skills in Python, Spark/PySpark, and SQL.
Experience working with modern cloud-based data platforms such as Databricks, Snowflake, or similar data lake environments.
Experience building and deploying solutions within AWS and Azure cloud environments.
Familiarity with containerization and development tools including Kubernetes, Docker, and Git.
Experience with data visualization and business intelligence tools, with Tableau preferred.
Ability to thrive in ambiguous environments while driving clarity, prioritization, and business outcomes.
Exceptional communication and storytelling skills with the ability to translate complex analytical concepts into practical, executive-level recommendations.
Experience mentoring and providing technical leadership to data scientists and analytics professionals.
Strong analytical thinking, problem-solving capabilities, and attention to detail.
Ability to manage multiple priorities and deliver results in a fast-paced environment.
Proven success collaborating across Product, Marketing, Sales, IT, Risk, Compliance, and other business functions.
Passion for fostering a data-driven culture built on rigorous experimentation, innovation, and continuous improvement.

Nice To Haves

PhD in Statistics, Data Science, or a related quantitative discipline strongly preferred.
Relevant data science, machine learning, or cloud certifications are a plus.
Familiarity with data science platforms such as Dataiku is a plus.

Responsibilities

Own the end-to-end design, execution, and analysis of marketing and product experiments, including A/B, multivariate, geo, and holdout testing.
Define and manage the experimentation roadmap, including hypothesis development, prioritization, power analysis, and success metrics.
Develop and refine methodologies for lift measurement, incrementality testing, and causal inference.
Establish standards, governance, and best practices that ensure statistical rigor and actionable insights across the organization.
Partner with stakeholders to build a culture of experimentation and data-driven decision making.
Design, build, validate, and deploy machine learning and Generative AI solutions using large-scale structured and unstructured data.
Develop predictive, prescriptive, attribution, market mix, and uplift models that drive measurable business outcomes.
Lead hands-on model development, coding, testing, and production deployment while establishing technical standards for the broader team.
Create and maintain robust data pipelines to ensure data quality, integrity, and scalability.
Continuously evaluate emerging technologies and analytical approaches to improve performance and business value.
Collaborate with Product, Marketing, Sales, IT, Risk, and Compliance teams to identify, prioritize, and solve high-value business opportunities.
Translate complex business challenges into testable hypotheses and analytical solutions.
Present findings and recommendations to both technical and executive audiences in a clear and compelling way.
Serve as a trusted advisor and subject matter expert on experimentation, analytics, AI, and machine learning.
Mentor and elevate fellow data scientists through coaching, technical guidance, and best-practice sharing.