A Data Scientist who ships to production, not just notebooks.
Churn models, LTV prediction, attribution and executive dashboards — built to be used, not just presented once.
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50+ founders consulted last month
Most data science work dies in a Jupyter notebook. I build models and pipelines that plug into your actual decisions — pricing, retention, marketing spend — and keep running after I leave.
What you get
Every engagement is built around measurable outcomes — not just deliverables.
Predictive models that matter
Churn, LTV, and demand forecasting models tied directly to a business decision.
Clean, trustworthy data
ETL pipelines and data warehousing so your numbers are consistent across every dashboard.
Executive dashboards
Looker/Metabase dashboards leadership actually opens every week.
Production-ready, not just notebooks
Models deployed as APIs or scheduled jobs — not a one-off analysis.
Models that plug into decisions
A model is only useful if someone acts on it. I start from the business decision — who to retain, what to price, where to spend — and work backwards to the model, the pipeline, and the dashboard that puts it in front of the right person every week.
What’s included
- Churn, LTV and demand forecasting models
- ETL pipelines and data warehousing
- Executive dashboards (Looker, Metabase)
- Production deployment as APIs or scheduled jobs
Quick answer
This is a data science engagement focused on models that ship to production — churn prediction, LTV forecasting, demand models and the dashboards that put them in front of decision-makers — not one-off analysis in a notebook. Every model starts from a specific business decision it needs to inform, so the output gets used rather than filed away.
How this compares to a one-off analytics project
- A one-off analysis answers a single question once; a production model keeps answering it every week as new data arrives
- Notebook-based analysis has no monitoring, so accuracy silently degrades; production deployment includes drift detection and retraining
- One-off projects rarely include a dashboard leadership will actually open; this engagement builds the dashboard as part of the deliverable
- Ad-hoc analytics work is judged on interesting findings; this work is judged against one agreed business metric, like reduced churn or lower CAC
What’s included, in full
- Churn, LTV and demand forecasting models
- ETL pipelines and data warehousing so numbers are consistent across dashboards
- Executive dashboards built in Looker or Metabase
- Production deployment as APIs or scheduled jobs
- Drift monitoring so the model stays accurate as your data changes
What a data scientist actually does day to day
The role of a data scientist is frequently misunderstood as simply "working with data" or "building dashboards," but the actual discipline involves formulating precise questions, cleaning and validating data before any analysis begins, applying appropriate statistical or machine learning methods, and — critically — translating findings into recommendations that non-technical stakeholders can act on with confidence.
Descriptive, predictive, and prescriptive analytics compared
| Type | Question answered | Typical use case |
|---|---|---|
| Descriptive | What happened? | Monthly sales reporting |
| Predictive | What will happen? | Demand forecasting |
| Prescriptive | What should we do? | Optimal inventory recommendations |
Many businesses jump straight to wanting predictive models without first establishing solid descriptive foundations, leading to models built on poorly understood or improperly cleaned data.
Typical engagement timeline
Common misconception about data quality
Handling messy, real-world business data
Business data in the real world is rarely clean — missing values, inconsistent formatting, and duplicate records are the norm rather than the exception. Practical, tested techniques for handling this messiness form a core part of this service, rather than assuming pristine, analysis-ready data will simply be handed over.
Choosing the right statistical method for the question
A common mistake is reaching for the most sophisticated available technique rather than the simplest method that actually answers the business question at hand. This service emphasizes matching method complexity to the actual problem, avoiding unnecessary complexity that makes results harder to interpret and trust.
Communicating results to non-technical stakeholders
A statistically sound analysis has little value if decision-makers can't understand or act on it. Significant emphasis is placed on translating technical findings into clear, actionable language that executives and non-technical stakeholders can confidently use to make decisions.
Bias and ethical considerations in data analysis
As data increasingly informs decisions with real human impact — hiring, lending, pricing — awareness of potential biases in datasets and analytical approaches has become an essential rather than optional consideration in any serious data science engagement.
Building reproducible analysis workflows
Analysis that can't be reproduced or re-run as new data arrives has limited long-term value. This service emphasizes building reproducible workflows and documentation, rather than one-off analyses that become obsolete or unverifiable the moment underlying data changes.
Who this data scientist service is for
- Businesses sitting on years of unused data wanting to extract actionable insight
- Teams needing a rigorous second opinion on an existing internal analysis
- Organizations building their first data-informed decision-making process
Setting realistic expectations about what data can answer
Not every business question can be answered from available data, and part of this service involves honestly identifying which questions are actually answerable given data quality and availability, rather than promising insights the underlying data simply cannot support.
Industry-specific data science considerations
Working with healthcare data involves different regulatory and ethical considerations than working with retail transaction data. This service adapts its approach to the specific regulatory environment, data sensitivity, and domain-specific norms of each client's industry rather than a one-size-fits-all methodology.
Working alongside existing internal data teams
This service is designed to complement internal data and analytics teams, providing specialized statistical or machine learning expertise for specific projects rather than replacing capabilities an internal team already has in place.
Pricing structure and engagement models
Engagements are scoped around specific deliverables and timelines agreed upfront, with transparent communication about progress and any adjustments needed as the data reveals more about what's actually achievable.
Visualization as a communication tool, not decoration
Data visualization is sometimes treated as a way to make a report look more polished, when its actual purpose is to communicate findings clearly and honestly. Charts and graphs are chosen based on what best represents the underlying data accurately, not based on which looks most visually striking in a presentation.
A/B testing and experimental design
Many business decisions could be validated through proper experimentation rather than relying on intuition or incomplete historical data. Practical guidance on designing valid A/B tests — including adequate sample sizes and avoiding common statistical pitfalls — forms part of building a genuinely data-informed decision culture.
Time series analysis for forecasting
Forecasting future business metrics requires specific techniques distinct from standard regression analysis, particularly when seasonality, trends, and cyclical patterns are present in the underlying data. Appropriate time series methods are applied based on the actual characteristics of each dataset rather than a generic forecasting approach.
Building a data-driven culture beyond a single project
A single analysis, however well done, has limited lasting impact if the organization doesn't build habits around continuing to ask data-informed questions. Part of this service includes knowledge transfer aimed at building lasting internal capability, not just delivering a one-off report.
Model interpretability vs raw predictive power
The most accurate model isn't always the right choice if stakeholders can't understand or trust why it makes the recommendations it does. A deliberate tradeoff between interpretability and raw predictive power is made based on the actual decision context, favoring simpler, explainable models when trust and buy-in matter more than marginal accuracy gains.
Handling small sample sizes honestly
Not every business has years of historical data to draw on. Honest communication about the statistical limitations of small sample sizes — and what conclusions can and cannot be reliably drawn from them — is part of maintaining analytical integrity rather than overstating confidence in limited data.
Cross-functional collaboration during analysis
The best data science outcomes emerge from close collaboration with the domain experts who understand the business context behind the numbers, rather than analysis conducted in isolation from the people who will ultimately use and act on the findings.
Final thought on building genuine data literacy
The lasting value of this service isn't just the specific analysis delivered, but the improved data literacy and healthier skepticism toward unverified numbers that clients carry forward into every future decision they make.
Common mistakes businesses make with data before seeking help
Onboarding process for new clients
What makes a data science engagement successful long-term
The most successful long-term engagements involve clients who commit to actually acting on findings and integrating data-informed thinking into ongoing decisions, rather than treating a single analysis as a checkbox exercise disconnected from real decision-making.
How this differs from hiring a full-time data scientist
| Aspect | Full-time hire | This service |
|---|---|---|
| Cost structure | Ongoing salary and benefits | Scoped engagement based on project |
| Breadth of expertise | Limited to one person's background | Draws on experience across many industries and problem types |
Businesses with periodic rather than continuous analytical needs often find this scoped model more cost-effective than committing to the ongoing cost of a full-time hire.
Scaling the engagement as data maturity grows
As an organization's data infrastructure and analytical maturity grow, the scope of engagement can expand accordingly — from initial exploratory analysis toward more sophisticated predictive modeling — rather than remaining static regardless of the organization's evolving needs.
Confidentiality of client data
Staying current with evolving methods and tools
Statistical and machine learning methods, along with the tools used to apply them, evolve continuously. Staying current with these developments and applying genuinely appropriate techniques — rather than defaulting to outdated approaches out of habit — is an ongoing professional responsibility.
Final thought for businesses considering this service
The value of good data science isn't the sophistication of the model used — it's the quality and honesty of the decision it ultimately informs. Businesses that get the most value from this service are those who genuinely act on findings, even when those findings challenge existing assumptions.
Documentation and knowledge transfer at engagement close
Every engagement concludes with clear documentation of methodology, assumptions, and findings, ensuring the client's team can understand, verify, and build upon the work independently rather than being left with an unexplained black-box result.
Handling conflicting stakeholder priorities
Different stakeholders within the same organization sometimes want the data to support different, conflicting conclusions. Maintaining analytical integrity in these situations — presenting what the data actually shows rather than what any particular stakeholder wants to hear — is a core professional commitment.
Can findings be presented directly to a board or investors?
Yes — findings can be packaged specifically for presentation to a board or investor audience, with appropriate framing and a level of technical detail suited to that specific audience's needs.
From kickoff to results
A clear, transparent process — no surprises.
Data audit
Map every data source, check quality, and identify the highest-value modelling opportunity.
Model prototyping
Build and validate models against a clear success metric, fast.
Production deployment
Ship the model as an API, batch job, or dashboard your team can rely on.
Monitor & retrain
Set up drift monitoring so the model stays accurate as your data changes.
01What tools do you use?
Python (pandas, scikit-learn, PyTorch where needed), SQL, and BI tools like Looker Studio or Metabase.
02Can you work with our existing data warehouse?
Yes — Snowflake, BigQuery, Postgres, or a spreadsheet-based setup; I’ll work with what you have.
03Do you need a data engineering team in place?
No — I can build the pipelines myself for small-to-mid scale, and hand off documentation for your team.
04How do you measure success?
We agree on one business metric upfront — reduced churn, higher LTV, lower CAC — and the model is judged against that, not accuracy alone.
05How much does a data science engagement cost?
Project-based work for a single model — churn, LTV or forecasting — typically starts around ₹1.5–2.5L, depending on data quality and complexity. Ongoing retainers for pipeline maintenance and monitoring are priced separately once the initial model is in production.
06How is this different from hiring a full-time data scientist?
A full-time hire is a good long-term investment once you have enough ongoing modelling work to justify a salary. This gets a production-ready model shipped in weeks without a multi-month hiring process, and you can bring on a full-time hire later using the pipeline and documentation already in place.
07What’s a typical engagement length?
A single model — churn or LTV, for example — usually takes 4–8 weeks from data audit to production deployment. Ongoing monitoring and retraining can continue as a lighter monthly retainer after that.
08Who is this not a good fit for?
Companies without any structured data yet — no CRM, no event tracking, no order history. Modelling needs real historical data to work with; if that doesn’t exist, the first step is instrumentation, not modelling.
09What happens in the first week?
A data audit — mapping every source, checking data quality, and identifying the highest-value modelling opportunity. You get a clear read on what’s actually possible with your current data before any modelling work starts.
10Is more data always better for building models?
No — a smaller, clean, well-understood dataset typically outperforms a larger dataset riddled with inconsistencies and gaps.
11What's the difference between predictive and prescriptive analytics?
Predictive analytics forecasts what will happen; prescriptive analytics recommends what action to take in response.
12What's the most time-consuming part of a typical engagement?
Data audit and cleaning — establishing a trustworthy foundation before any modeling begins.
13Is real-world business data usually clean and ready for analysis?
No — missing values, inconsistent formatting, and duplicates are the norm; handling this messiness is a core part of the service.
14Should the most sophisticated statistical method always be used?
No — the simplest method that actually answers the business question is preferred, avoiding unnecessary complexity that hurts interpretability.
15Are bias and ethical considerations addressed in the analysis?
Yes — as data increasingly informs decisions with real human impact, bias awareness is treated as essential rather than optional.
16Can every business question be answered from available data?
No — part of this service involves honestly identifying which questions are actually answerable given data quality, rather than overpromising.
17Are industry-specific regulations considered in the analysis?
Yes — the approach adapts to the specific regulatory environment and data sensitivity of each client's industry.
18Can this work alongside an existing internal data team?
Yes — designed to complement internal teams with specialized expertise for specific projects rather than replacing existing capabilities.
19Is data visualization just about making reports look nice?
No — its purpose is to communicate findings clearly and honestly; charts are chosen for accuracy, not visual polish alone.
20Is A/B testing guidance included?
Yes — practical guidance on valid experimental design, including adequate sample sizes and avoiding common statistical pitfalls.
21Does this service help build lasting internal data capability?
Yes — knowledge transfer is included to build lasting habits around data-informed decisions, not just deliver a one-off report.
22Is the most accurate model always chosen?
Not necessarily — when trust and stakeholder buy-in matter more than marginal accuracy gains, simpler, more interpretable models are favored.
23What if a business only has a small amount of historical data?
Honest communication about the statistical limitations of small samples ensures conclusions aren't overstated beyond what the data can support.
24What's a common mistake businesses make before seeking data help?
Collecting vast amounts of data without defining what decisions it's meant to inform, resulting in dashboards nobody actually uses.
25What does the onboarding process look like?
An initial call on the actual business questions, a data audit for quality and completeness, then scoping a realistic analysis plan.
26How does this compare to hiring a full-time data scientist?
This scoped engagement is often more cost-effective for businesses with periodic rather than continuous analytical needs.
27Can the engagement scale as data maturity grows?
Yes — scope can expand from initial exploratory analysis toward more sophisticated predictive modeling as needs evolve.
28Is client data kept confidential?
Yes — all data and findings are treated as strictly confidential, handled with appropriate data security practices.
29Are methods kept current with the field's evolution?
Yes — staying current with evolving statistical and machine learning methods is an ongoing professional responsibility.
30Is model sophistication the main measure of value?
No — the quality and honesty of the resulting decision matters far more than how sophisticated the underlying model is.
31Can this service work with data spread across multiple systems?
Yes — consolidating and reconciling data spread across multiple systems is a common starting point for many engagements.
32Is there a minimum data volume required to start?
No — even modest datasets can yield useful insight when analyzed rigorously; volume matters less than data quality and relevance.
33Is documentation provided at the end of an engagement?
Yes — clear documentation of methodology and findings ensures the client's team can understand and build upon the work independently.
34Can findings be presented directly to a board or investors?
Yes — findings can be packaged specifically for that audience, with framing and technical detail suited to their needs.
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Book a free 30-minute strategy call. No pitch, no pressure — just honest advice on where to focus.