Decision science
Hire Data Scientists who turn product and business data into decisions.
Get a dedicated Data Scientist to define metrics, analyze behavior, build models, design experiments, forecast outcomes, and explain what changed and why. Shortlist in 48 hours. Two-week paid trial in your codebase/data environment. Starts at $2,500/mo.
Direct answer
What does Data Scientist own?
A Data Scientist is the right hire when the business needs defensible decisions from messy product, customer, operational, or revenue data. This role owns metric design, data quality review, exploratory analysis, experiments, forecasts, predictive models, segmentation, causal reasoning where appropriate, and decision memos.
Hiring problem
Hire this role when decisions need evidence, not dashboards alone.
Teams collect data but still make roadmap, growth, risk, and product decisions by opinion because metrics are messy, experiments are weak, and models are not tied to business decisions.
- Metric design
- Data quality review
- Exploratory analysis
- Experiment design
- Forecasting
- Predictive models
- Segmentation
- Causal analysis where appropriate
- Decision memos
- Dashboard/model handoff
- Pure dashboard production without analysis
- LLM app engineering
- RAG pipelines
- App feature delivery
- Data engineering platform-only work
First 14-day proof
The trial should create evidence, not just activity.
Data quality audit
Shows missingness, drift, duplication, outliers, leakage, schema issues, and metric trust. Every later analysis is only as good as this, so it comes first.
Metric tree
Connects a business outcome to the product, user, and operational metrics that drive it. It stops the team from optimizing a number that does not matter.
Baseline analysis
Establishes the current state before any modeling, experiment, or roadmap change, so improvement can actually be proven later.
First model or experiment design
Defines what can be tested, predicted, or estimated — and what cannot. A weak version overclaims; a strong version is honest about limits.
Decision memo
Explains what the data supports, what it does not, and what action is recommended. The deliverable is a decision, not a dashboard.
Next data gaps
Lists missing data, instrumentation gaps, and analysis risks so the team knows what to capture before the next question.
Default stack
Stack fluency for Data Scientist work.
The exact tools follow your environment. These are the common surfaces we vet against for this role.
Use cases
Where this hire creates leverage.
The best use case is one where the role can own a clear first proof during the paid trial.
Product analytics
Understand adoption, retention, activation, conversion, and product behavior with metrics the team can trust.
Churn and retention modeling
Identify customers or users at risk and which signals explain it, ending in a decision memo, not just a model.
Pricing analysis
Use evidence to support pricing, packaging, or monetization decisions instead of guessing at willingness to pay.
Forecasting
Predict demand, revenue, workload, or operational volume with stated confidence and assumptions.
Experiment design
Design tests that avoid bad samples, weak metrics, and false confidence — so the result actually settles the question.
Customer segmentation
Group customers or users by behavior, value, risk, or need to make targeting and roadmap calls defensible.
Operational decision science
Improve decisions in staffing, support, sales, logistics, or internal operations with analysis tied to a clear action.
Transparent pricing
Pick seniority by ownership, not mystery quotes.
Supervised delivery for clear implementation work.
Independent feature ownership for production AI work.
High-judgment ownership for ambiguous or risky AI delivery.
Outcome clarity
What should change after you hire this role?
The decision has a defensible metric base.
Data quality gaps are explicit.
Analysis produces a memo, not just charts.
Adjacent-role comparison
When another AI role is the better hire.
LLM Engineer
Choose LLM Engineer if model behavior is the bottleneck.
PLTAI Platform Engineer
Choose AI Platform Engineer if ML/AI infrastructure is the bottleneck.
APPAI Application Engineer
Choose AI Application Engineer if the output needs productization.
FDEForward-Deployed AI Engineer
Choose Forward-Deployed AI Engineer if discovery and rollout ownership are part of the work.
Vetting criteria
Screened for this role’s failure modes.
Metric design
Data quality skepticism
Experiment judgment
Model/business fit
Decision communication
Interview questions
Use the interview to test judgment.
- How would you define the metric tree for an AI feature?
- What makes an experiment invalid?
- How do you explain model limits to leadership?
- What should a first decision memo include?
Hiring flow
From scope to paid trial.
30-minute role scope
Map the AI workflow, current stack, first deliverable, security boundaries, seniority, and the role that should own the work.
2-3 vetted engineers
Receive a short list with matching rationale. The goal is fewer names with stronger fit, not resume volume.
Paid trial in your codebase
The selected engineer works inside your repo, rituals, issue tracker, and review process so fit is judged by real work.
Continue, replace, pause, or scale
Continue month-to-month, request a free replacement, pause without a long lock-in, or add adjacent roles.
Security, IP, governance
Repo access is scoped before the engineer starts.
NDA, IP assignment, repository access, communication channels, data boundaries, and AI tool rules are clarified before onboarding. Devlyn avoids unverified compliance claims and works within buyer-controlled systems.
FAQ
Questions before you hire Data Scientist.
How is this different from a dashboard builder?
A Data Scientist owns the decision: metric design, analysis, experiments, models, forecasts, and the memo explaining what changed and why.
Can they work in our warehouse?
Yes. The trial can happen in your warehouse, notebooks, BI tool, or approved data environment with scoped access.
When do we need a Data Engineer instead?
If pipelines are missing before analysis can start, you may need data engineering support first. Devlyn does not create a separate role unless the business approves it.
How fast can I see Data Scientist candidates?
After the role scope, Devlyn targets two or three vetted profiles within 48 hours.
What does the two-week paid trial include?
The trial should produce role-specific proof for Data Scientist work inside your actual repo, data environment, or approved workflow.
Can the engineer work in our repository?
Yes. Repo access, communication channels, data boundaries, NDA, and IP assignment are scoped before onboarding.
What if fit is wrong?
You can request a free replacement instead of being forced through a long lock-in or conversion fee.
What does pricing include?
Pricing covers one dedicated AI-native engineer. Junior starts at $2,500/mo, mid at $3,500/mo, and senior at $4,500/mo.
Final CTA
Tell us the AI workflow. We’ll confirm whether Data Scientist is the right hire.
If another role is a better fit, the role scope should catch that before you interview anyone.