Senior Director Data Science

Circana

20h•Remote

About The Position

Circana is a leading provider of technology, AI, and data solutions for consumer packaged goods companies, manufacturers, and retailers. Our predictive analytics and Liquid Data® platform help clients measure market share, uncover consumer behavior, and drive growth—powered by six decades of expertise and an expansive, high-quality data set. At Circana, we are fueled by our passion for continuous learning and growth, we seek and share feedback freely, and we celebrate victories both big and small in an environment that is flexible and accommodating to our work and personal lives. We’re a global company dedicated to fostering inclusivity and belonging. We value and celebrate the unique experiences, cultures, and viewpoints that each individual brings. By embracing a wide range of backgrounds, skills, expertise, and beyond, we create a stronger, more innovative environment for our employees, clients, and communities. With us, you can always bring your full self to work. Join our inclusive, committed team to be a challenger, own outcomes, and stay curious together. Circana is proud to be Certified™ by Great Place To Work®. This prestigious award is based entirely on what current employees say about their experience working at Circana. Learn more at www.circana.com. The Sr. Director, Data Science role supports and manages highly complex software development initiatives, including data engineering, data analysis, user support, and enterprise‑scale data solutions. This role manages and drives integrated teams, delivering high‑quality technology solutions across big data platforms.

Requirements

10+ years of software development experience.
5+ years of hands‑on experience developing ETL Spark applications in PySpark or Scala.
5+ years managing globally distributed teams of 4+ developers, including mentorship, performance oversight, and delivery management.
Strong understanding of Spark architecture, data frames, distributed processing, and performance tuning.
Experience designing, developing, and supporting data‑driven analytical applications in big data environments.
Experience with relational databases such as Oracle or PostgreSQL.
Experience reviewing complex code, troubleshooting software/data issues, and ensuring on‑schedule delivery.
Hands‑on software experience with Spark (Databricks), Python, Scala, SQL, UNIX shell scripting, SQL scripting.
Experience with cloud platforms (Azure, AWS).
Expertise in database design, data modeling, and workflow/diagramming tools (flowcharts, data flows).
Familiarity with enterprise data platforms such as Synapse, Snowflake, Google BigQuery, and tools like Ab Initio.
Experience using SDLC methodologies including Agile, Scrum, Kanban, and Waterfall.
Excellent leadership skills with proven ability to manage onshore/offshore engineering teams.
Telecommuting permitted

Responsibilities

Designing, developing, and maintaining highly scalable ETL applications on Hadoop/Spark environments.
Delivering big data projects using Spark, Python, Scala, SQL, and Hive within distributed computing ecosystems.
Leading and mentoring onshore and offshore development teams, ensuring high‑quality delivery and consistent engineering standards.
Actively participating in—and at times leading—Agile/Scrum ceremonies, including daily stand‑ups, sprint planning, retrospectives, and code reviews.
Tuning and optimizing ETL pipeline performance to meet strict SLAs across highly complex, concurrent processing environments.
Engaging with business partners to understand needs, translate requirements, and identify appropriate technology solutions.
Coordinating development project execution from initiation through release, including dependency planning and sprint scheduling.
Managing team velocity, financials, and other critical delivery metrics.
Reviewing product backlog, designs, code, and test results to mitigate delivery risks and ensure product quality.
Leading adoption of innovative technologies to support evolving software product and data platform needs.
Assessing, installing, and enhancing software required for the organization’s broader big data platform.
Building robust ETL pipelines supporting day‑to‑day client delivery operations.