IT Leader, Data Science

Dow•Midland, MI

About The Position

Dow is seeking an IT Leader, Data Science for its Data Science & Engineering team within the Enterprise Data & AI organization. The position is located in Houston, Texas; Midland, Michigan; or Champaign, Illinois, and may also support Virtual Office. The role manages and develops a team of data scientists delivering advanced analytics and data science solutions that create measurable value for Dow. Its primary focus is people leadership, team effectiveness, portfolio stewardship, stakeholder partnership, and delivery accountability rather than serving as the team’s lead technical practitioner. The leader applies a working understanding of data science teams and their needs to represent the team to business and technology leaders, advocate for priorities and resources, and foster a safe, inclusive, performance-driven culture. The position establishes and measures operating discipline across the data science development lifecycle so the portfolio is prioritized, governed, communicated, and delivered effectively. The role partners with business and functional leaders to identify and prioritize opportunities, maintains awareness of relevant data science and emerging AI capabilities, and communicates team capacity, priorities, trade-offs, risks, and contributions. It owns portfolio prioritization and delivery practices, and works with senior practitioners and adjacent technology teams to strengthen practices and address technical risks. The leader ensures appropriate standards, tools, expertise, and partnerships are in place for modern data science, software engineering, and MLOps practices. The position does not offer relocation assistance. Dow describes its total rewards as including competitive pay and bonus opportunities, wellbeing programs, retirement and insurance offerings, learning and growth opportunities, flexible work culture, vacation and paid time off, and other location-dependent programs.

Requirements

Bachelor’s degree, relevant military experience at E5 rank/Petty Officer 2nd Class or higher, or 12 years of experience in lieu of a bachelor’s degree.
At least three years of people management experience, including direct responsibility for coaching, performance management, development planning, and team outcomes.
Demonstrated experience leading complex, cross-functional initiatives involving business and technology stakeholders.
Ability to work legally in the United States; visa sponsorship or support is not available, including for U.S. permanent residency processes.
Leadership understanding of data science teams, their work and lifecycle, risks, and support needs, without needing to serve as the primary technical expert.
Ability to develop talent, set clear expectations, provide constructive feedback, build trust, and hold a team accountable for results.
Ability to build credibility, represent a team, communicate capacity and trade-offs, and translate between business needs and technical expertise.
Ability to balance priorities, resources, risks, dependencies, value, team sustainability, and stakeholder expectations.
Ability to establish governance, accountability, and measures that improve quality, predictability, transparency, and results.
Ability to connect investments to enterprise priorities, remove barriers, and foster a safe, inclusive, collaborative, improvement-driven culture.

Nice To Haves

Degree in IT, Computer Science, Analytics, Engineering, Business, Chemistry, or a related discipline.
Experience managing high-skill technical, analytical, digital, or scientific teams; direct leadership of data scientists or advanced analytics professionals is preferred.
Working knowledge of data science solution lifecycles, software engineering, MLOps, model governance, product delivery, and associated risks.
Experience managing a portfolio of data science, analytics, AI, or digital initiatives, including prioritization, governance, resource planning, risk management, and value tracking.
Proven ability to build effective teams, represent technical capabilities to business leaders, and translate ambiguous challenges into actionable work.
Strong consulting, facilitation, and communication skills, with success influencing across a large, complex enterprise and leading through ambiguity.
Commitment to continuous learning, talent development, inclusion, and improvement.

Responsibilities

Lead, coach, and develop data scientists through clear expectations, feedback, career guidance, and accountability for results.
Create a safe, inclusive, high-performing environment that supports learning, innovation, constructive challenge, and continuous improvement.
Remove organizational barriers and support workforce planning, hiring, onboarding, recognition, succession, and capability development.
Build trusted partnerships with business and functional leaders to identify, frame, and prioritize data science opportunities with clear outcomes, success measures, and ownership.
Maintain awareness of relevant data science and emerging AI capabilities and help stakeholders assess where they can create responsible, sustainable value.
Represent the team to technical and non-technical audiences, communicating capabilities, capacity, priorities, trade-offs, risks, and contributions.
Own and prioritize the team’s portfolio, balancing enterprise needs, business value, capacity, dependencies, development needs, and delivery risk.
Establish disciplined practices for intake, planning, governance, status reporting, risk management, and executive communication across the data science lifecycle.
Ensure initiatives have clear objectives, ownership, stakeholder engagement, measurable outcomes, delivery plans, and decision points.
Lead portfolio reviews and adjust priorities or resources as business needs and delivery conditions change.
Partner with senior practitioners and adjacent technology teams to set direction, clarify responsibilities, remove barriers, strengthen reusable practices, and address material technical risk.
Set expectations for fit-for-purpose documentation, governance, model validation, production readiness, monitoring, support, and retirement.
Ensure the team has the standards, tools, expertise, and partnerships needed to apply modern data science, software engineering, and MLOps practices.
Use meaningful measures and stakeholder feedback to improve delivery, adoption, team effectiveness, operating discipline, and business value.

Benefits

Equitable, market-competitive base pay and bonus opportunity, along with locally relevant incentives.
Programs supporting physical, mental, financial, and social well-being.
Competitive retirement program that may include company-provided benefits, savings opportunities, financial planning, and educational resources.
Employee stock purchase programs, depending on location.
Student Debt Retirement Savings Match Program in the United States, applying the value of monthly student debt payments as if they were contributions to the Employees’ Savings Plan (401(k)).
Medical and life insurance packages, with a variety of coverage options; travel insurance is available in certain countries or locations.
Training, mentoring, work experiences, community involvement, and team-building opportunities.
Role-based flexibility intended to support productivity and personal needs.
Competitive yearly vacation allowance.
Paid time off for new parents, including adoptive and foster parents.
Paid time off to care for sick or injured family members.
Paid time off for volunteering and Employee Resource Group participation.
Wellbeing Portal for Dow employees.
On-site fitness facilities, depending on location.
Employee discounts for online shopping, cinema tickets, gym memberships, and more.
Some locations may offer transportation allowances, meal subsidies or vouchers, and carbon-neutral transportation incentives.