Overview: Data Scientist Services in Falnir

Models that plug into decisions

I start from the business decision your Falnir team is trying to make, then work backwards to the model, the pipeline, and the dashboard that puts it in front of the right person every week. Most data science work dies in a notebook — the goal here is a model that changes a real decision, on a schedule, without babysitting.

What's included

Predictive models

Churn, lifetime value and demand forecasting models built on your Falnir business's actual historical data, validated against real outcomes.

Model types

• Churn and retention risk scoring
• Customer lifetime value (LTV) prediction
• Demand and inventory forecasting

Data pipelines and warehousing

Clean, reliable ETL pipelines so your Falnir team's models are always running on current data, not a stale export.

Pipeline components

• Ingestion from CRM, product and billing systems
• Warehousing (Snowflake, BigQuery, or Postgres)
• Scheduled transformation and quality checks

Executive dashboards

Dashboards built for Falnir leadership to actually check weekly — not a data team's internal exploration tool.

Production deployment

Models shipped as APIs or scheduled jobs your Falnir engineering team can rely on, with monitoring for drift and failure.

How the engagement runs

Process

Decision mapping

We start from the specific decision your Falnir business needs to make better — not a generic "let's do data science" brief.

Model build & validation

Models are validated against held-out data and, where possible, a real business outcome before going live.

Deployment & handover

Production deployment with documentation your Falnir team can maintain independently.

Who this is for

Subscription and D2C businesses

Falnir companies that need churn and LTV models tied directly to retention and marketing spend decisions.

Operations-heavy businesses

Falnir teams that need demand forecasting to plan inventory, staffing or supply chain decisions.

Tools & technology

Core stack

Languages & frameworks

Python (pandas, scikit-learn, PyTorch where needed), SQL

The simplest tool that reliably solves your Falnir business's problem — not the most fashionable one.

Note

BI layer is typically Looker Studio or Metabase, chosen to match what your Falnir team already uses.

Quick answer

Hiring a data scientist here means getting models and pipelines that plug directly into a specific business decision, not exploratory analysis that stays in a notebook. For a Falnir business, that means churn, LTV or demand forecasts are validated, deployed as APIs or scheduled jobs, and monitored — so they keep informing pricing, retention or inventory decisions long after the initial build.

How this compares to a data analyst producing reports

• A data analyst summarizes what already happened; this builds predictive models — churn scoring, LTV, demand forecasting — that inform what your Falnir business should do next.
• Analyst reports are typically manual and one-off; pipelines here run on a schedule against your Falnir business's live CRM, product and billing data, not a stale export.
• A report sits in a slide deck; models here are shipped as production APIs or scheduled jobs your Falnir engineering team can rely on, with monitoring for drift.
• Analysts rarely own deployment; this includes production deployment and documentation so your Falnir team can maintain the pipeline independently.

Why Falnir businesses choose Deepak Suhag

Models and pipelines that plug into actual decisions — pricing, retention, marketing spend — and keep running long after the engagement ends.