FAQs: Data Scientist

Python (pandas, scikit-learn, PyTorch where needed), SQL, and BI tools like Looker Studio or Metabase.

Yes — Snowflake, BigQuery, Postgres, or a spreadsheet-based setup; I’ll work with what you have.

No — I can build the pipelines myself for small-to-mid scale, and hand off documentation for your team.

We agree on one business metric upfront — reduced churn, higher LTV, lower CAC — and the model is judged against that, not accuracy alone.

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.

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.

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.

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.

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.

No — a smaller, clean, well-understood dataset typically outperforms a larger dataset riddled with inconsistencies and gaps.

Predictive analytics forecasts what will happen; prescriptive analytics recommends what action to take in response.

Data audit and cleaning — establishing a trustworthy foundation before any modeling begins.

No — missing values, inconsistent formatting, and duplicates are the norm; handling this messiness is a core part of the service.

No — the simplest method that actually answers the business question is preferred, avoiding unnecessary complexity that hurts interpretability.

Yes — as data increasingly informs decisions with real human impact, bias awareness is treated as essential rather than optional.

No — part of this service involves honestly identifying which questions are actually answerable given data quality, rather than overpromising.

Yes — the approach adapts to the specific regulatory environment and data sensitivity of each client's industry.

Yes — designed to complement internal teams with specialized expertise for specific projects rather than replacing existing capabilities.

No — its purpose is to communicate findings clearly and honestly; charts are chosen for accuracy, not visual polish alone.

Yes — practical guidance on valid experimental design, including adequate sample sizes and avoiding common statistical pitfalls.

Yes — knowledge transfer is included to build lasting habits around data-informed decisions, not just deliver a one-off report.

Not necessarily — when trust and stakeholder buy-in matter more than marginal accuracy gains, simpler, more interpretable models are favored.

Honest communication about the statistical limitations of small samples ensures conclusions aren't overstated beyond what the data can support.

Collecting vast amounts of data without defining what decisions it's meant to inform, resulting in dashboards nobody actually uses.

An initial call on the actual business questions, a data audit for quality and completeness, then scoping a realistic analysis plan.

This scoped engagement is often more cost-effective for businesses with periodic rather than continuous analytical needs.

Yes — scope can expand from initial exploratory analysis toward more sophisticated predictive modeling as needs evolve.

Yes — all data and findings are treated as strictly confidential, handled with appropriate data security practices.

Yes — staying current with evolving statistical and machine learning methods is an ongoing professional responsibility.

No — the quality and honesty of the resulting decision matters far more than how sophisticated the underlying model is.

Yes — consolidating and reconciling data spread across multiple systems is a common starting point for many engagements.

No — even modest datasets can yield useful insight when analyzed rigorously; volume matters less than data quality and relevance.

Yes — clear documentation of methodology and findings ensures the client's team can understand and build upon the work independently.

Yes — findings can be packaged specifically for that audience, with framing and technical detail suited to their needs.