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About This Role
Grow with us. Lead with us.
At Pioneer, you’ll work directly with executive teams, solving complex problems and shaping strategies that reset what’s possible. Sometimes the work is headline\-worthy. Sometimes it’s foundational. But every project earns trust – and earns us the right to take on more. You’ll get the kind of access, challenge, and growth found at big firms – while helping build a company that’s scaling fast and guided by what we value.
If you’re looking for meaning, momentum, and a seat at the table, you’re in the right place.
The Managing Director (MD) of Data \& AI is the senior executive accountable for the performance, growth, and long\-term health of Pioneer’s Minneapolis Data \& AI practice. This role leads a newly unified team — formed by merging Pioneer’s established data engineering and analytics capability with its AI and machine learning practice — and is responsible for integrating these teams into a coherent, market\-leading offering.
The MD blends deep technical fluency in modern data platforms (Databricks, Microsoft Azure \& Fabric, Snowflake) with applied AI/ML expertise to deliver measurable outcomes for clients. This role serves as the primary growth engine and senior client relationship owner for data and AI work across the Minneapolis market, and contributes to the broader direction of Pioneer’s Technology Services strategy.
The MD brings consultative credibility and helps clients harness data and AI as a strategic asset. The MD leads with executive presence, builds pipeline through relationships and reputation, and ensures the practice delivers with quality, pace, and accountability.
CORE ACCOUNTABILITIES
The Managing Director owns market outcomes across revenue, margin, client satisfaction, team engagement, and practice depth. Primary focus areas are business development and senior client relationship management, with accountability for the full practice P\&L and the health of the newly merged team.
KEY RESPONSIBILITIESGrowth \& Business Development
- Own and grow the Minneapolis Data \& AI pipeline — from initial relationship to signed engagement
- Lead executive\-level client conversations across data strategy, AI enablement, and platform modernization
- Develop and execute the Data \& AI go\-to\-market strategy for Minneapolis, aligned to firmwide direction in collaboration with Pioneer’s National Solutions Team.
- Drive account planning and expansion within existing clients across sectors, with an eye toward industries where data and AI create high strategic value
- Represent Pioneer at industry events, conferences, and thought\-leadership forums to build market visibility
- Contribute to Pioneer’s overall Technology Services strategy, collaborating with market and practice leaders to bring integrated solutions to clients
Client Delivery \& Relationships
- Serve as executive sponsor on key client engagements, ensuring quality, risk management, and satisfaction
- Maintain senior\-level client relationships as a trusted advisor on data and AI strategy
- Set and uphold delivery excellence standards aligned to Pioneer’s delivery model and client satisfaction expectations
- Translate client business problems into concrete data and AI solutions leveraging Pioneer’s capabilities
- Oversee utilization strategy and resource deployment across the practice portfolio
Practice Leadership \& Team
- Lead the integration of Pioneer’s data engineering/analytics and AI/ML teams into a unified, high\-performing practice
- In partnership with Pioneer National Solutions team, refine the practice’s service offerings, delivery methodology, and technology platform standards (Databricks, Azure, Fabric, Snowflake)
- Build and retain a strong team of data engineers, analytics consultants, data scientists, and ML engineers
- Drive talent acquisition, onboarding, and career development in partnership with Pioneer’s talent processes
- Foster a culture of collaboration, continuous learning, and delivery accountability
- Own org design decisions and headcount planning as the practice scales
Planning \& Operations
- Own annual business planning for the Minneapolis Data \& AI practice, including revenue targets, headcount, and investment priorities
- Manage practice financials: revenue, margin, utilization, and pipeline health through Pioneer’s operating systems (PioHub)
- Participate in Pioneer’s Operating Model reviews and cross\-market resource management
- Maintain visibility into AI/ML project portfolio performance and delivery risk across client engagements
Innovation \& Offering Development
- Champion a product\-oriented mindset within the practice — applying product development lifecycle thinking to how Pioneer builds and scales data and AI solutions
- Identify opportunities to develop repeatable accelerators, frameworks, and IP across the data and AI stack
- Stay current on advancements in AI/ML, data engineering, and platform technology; translate trends into client\-relevant points of view
Collaborate with Pioneer’s broader AI team and firmwide leadership on cross\-portfolio innovation initiatives
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Requirements Required
- 15\+ years of experience in data, analytics, AI/ML consulting or professional services
- Demonstrated ability to grow revenue, manage a P\&L, and own client relationships at the senior executive level
- Hands\-on fluency with modern data platforms \& their AI capabilities — Databricks, Microsoft Azure \& Fabric, and/or Snowflake
- Experience leading or overseeing applied AI and machine learning engagements in a consulting or product context
- Track record of building, integrating, and scaling high\-performing technical consulting teams
- Strong executive presence with the ability to influence C\-suite stakeholders and win competitive work
- Entrepreneurial mindset with sound judgment, decisiveness, and comfort with ambiguity
- Thrives in a high\-accountability, growth\-oriented environment with a bias toward action
Preferred
- Industry experience in utilities, energy, or infrastructure — ideally with direct client relationships in the sector
- Exposure to product development lifecycle and product ownership practices within a consulting or technology firm
- Experience leading organizational integrations or practice mergers
- Experience working with technology partnership networks (e.g. Microsoft, Anthropic, etc.)
- Familiarity with AI governance, responsible AI frameworks, and enterprise AI deployment at scale
- MBA or advanced degree in a quantitative or technical discipline
TECHNOLOGY PLATFORM EXPERTISE
The following platforms represent Pioneer’s core delivery stack for this practice. Expertise in at least two is expected; the MD is expected to credibly lead client conversations across all.
- Databricks: Data lakehouse, unified analytics, and ML engineering
- Microsoft Azure \& Fabric: Cloud data platform, real\-time analytics, and end\-to\-end data integration
- Snowflake: Cloud data warehousing and data sharing
- AI / ML Tooling: Model development, MLOps, LLM orchestration, and enterprise AI deployment
- Low Code / No Code Platforms: Governance, training, deployments and process automation
\#LI\-EH1
Location:
Must be able to maintain hybrid schedule in Minneapolis, MN market.
Benefits
The estimated salary range for this role is $180,000\-285,000 annually, depending on factors such as skillset and experience. Bonuses and other incentives are awarded based on individual contributions and overall company performance.
Pioneer offers a comprehensive benefits package, including meaningful time off and paid holidays, parental leave, 401(k) with employer match, tuition reimbursement, and a broad range of health and welfare benefits, including medical, dental, vision, life, and disability coverage.
Salary Context
This $180K-$285K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Pioneer Management Consulting, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($232K) sits 28% above the category median. Disclosed range: $180K to $285K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Pioneer Management Consulting AI Hiring
Pioneer Management Consulting has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Denver, CO, US, Minneapolis, MN, US. Compensation range: $132K - $285K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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