01Weeks 1 to 2
Diagnose
We map your business goals, current team, data estate and platform stack. We identify the gaps that prevent delivery.
- Current team and skills map
- Data and platform maturity assessment
- Priority gaps ranked by business impact
Advisory Services
Talent Strategy and Placement
Most data and AI hiring problems start before a role is posted. We're technologists first and recruiters second, so we help you decide which roles your roadmap needs, in what order, and place people vetted by practitioners who've done the work.
01
Outcomes first
We work with you to clarify the key initiatives and platforms, then align on the specific roles and skill sets that best support them.
02
Build in sequence
Hiring out of order wastes a role and a year. We match hiring to how ready your data actually is.
03
Practitioner expert validation
Our data and AI practitioners assess leaders on leading teams and driving outcomes. For individual contributors, we assess platform skills, problem‑solving and cross‑functional collaboration.
04
Faster, de‑risked hiring
We shrink the time leaders spend reviewing candidates by delivering a vetted shortlist ready for decisions.
Who this is for
CIOs and VPs of Data, Analytics or AI at manufacturers and retailers building out a data and AI team. Also HR and recruiting leaders who want a practitioner's read on a role before it goes out.
01 / Our method
From roadmap to the right team
02Weeks 2 to 3
Design
We translate your roadmap into a team plan. We define roles, hiring sequence, seniority and how the team works together.
- Hiring sequence by quarter
- Outcome-based role scorecards
- Pay, title and structure guidance
03Weeks 3 to 6
Source
We approach proven candidates in our network and the wider market. This includes people not actively looking who would consider the right mission.
- Targeted market map
- Direct outreach to vetted profiles
- Weekly pipeline updates
04Ongoing
Vetted
Our advisors review every finalist before an introduction. You meet fewer candidates. Each can perform the role.
- Practitioner technical review
- Delivery and reference checks
- Our assessment of strengths and risks
05First 90 days
Place and integrate
We support offer closure and plan the first 90 days. We check in to support integration and retention.
- Offer and closing support
- 30, 60 and 90 day success plan
- Check-ins with you and the hire
02 / Hiring sequence
Hire for the stage you are in
Stage 1
Foundation
Data is held in spreadsheets and plant systems. Reports are difficult to trust.
Hire first
- Data engineer
- Data architect
- Analytics lead
Avoid: Hiring AI scientists before data is reliable.
Stage 2
Scale
A modern platform is in place. Demand for use cases exceeds delivery capacity.
Hire first
- Analytics engineers
- ML engineer
- Data product manager
Avoid: Adding headcount without clear ownership of each data product.
Stage 3
Optimize
Data is trusted. Models are live. Priorities are speed, value and governance.
Hire first
- Head of AI
- MLOps lead
- Data governance lead
Avoid: Treating AI as side projects with no path to the plant floor.
03 / Are you ready?
Hiring readiness check
Most data and AI searches fail before a candidate is ever contacted. If you can't check most of these, the gap is the brief, not the candidate pool. Resolve it before you post the role.
- We can name the business outcome this hire is accountable for, not just their job title
- We know our data and platform maturity stage, so we're not hiring ahead of or behind it
- We've agreed who this person reports to and who judges whether they succeeded
- The role has budget, executive sponsorship and real decision authority
- We've scored the role against the outcomes it must deliver, not the tools it touches
- Our process can close in weeks, because the people who can do this work are rarely on the market long
- We have the interview team confirmed, time blocked on their calendars, and a shared playbook and rubric ready.
- We have a 90 day plan ready before the person's first day, not after.
- We can name the specific use cases, the platforms involved, and their current state of maturity.
The right team, in the right order,
vetted by people who have done the work.