Our data scientists turn raw business and product data into forecasts, experiments, and decisions your team can act on, covering everything from exploratory analysis to production-grade predictive models.
They work in Python, SQL, and R across FinTech, Healthcare, Retail, and SaaS, partnering directly with product and analytics teams to answer questions the business is actually asking, not just build models for their own sake.
- 7+ years average experience in applied statistics and predictive modeling
- Analytics and forecasting projects delivered across 20+ industries
- End-to-end ownership from raw data to decision-ready dashboards
A Mumbai-Based Data Science Team Serving Global Markets
We are a product-focused software development company serving clients globally, with a dedicated data science engineering team delivering across time zones.
Our data scientists have built forecasting models, experimentation frameworks, and analytics pipelines across 20+ industries, working directly with founders, product leads, and analytics teams across the US and UK time zones on decisions with real business consequences.
- ISO/IEC 20000-1:2018 certified, structured delivery on every engagement
- AWS Partner, Clutch Global 2024, 4.9/5 client rating
- Data science delivery across FinTech, Healthcare, Retail, and SaaS globally
How We Onboard Your Data Scientist
Structured and fast, with no extended discovery phases before your data scientist starts working with your actual data.
Share Your Data & Questions
Tell us what decisions you're trying to make, what data you already have, and what tools your team uses. We map this to the right analytical profile immediately.
Scientist Matching
We shortlist data scientists based on domain experience, statistical method fit, and familiarity with your existing data stack and reporting tools.
Trial and Alignment
Run a paid trial sprint before committing. The scientist reviews your data sources, validates data quality, and aligns on the first analytical priority.
Active Analysis
From exploratory analysis to model validation and stakeholder readouts, the scientist works inside your sprint cadence, in your timezone, with your tools.
Conversation Analytics at Enterprise Scale
Data-Driven Candidate Scoring at Scale
Mple needed to replace subjective, interview-based hiring judgments with something measurable. This system could evaluate a candidate's communication and role-specific competencies consistently, without the bias and time cost baked into traditional recruiter-led interview processes.
We built the platform layer that evaluates candidate responses in real time and turns them into data-driven performance insights, combining AI-driven interview analysis with roleplay simulations, giving recruiters a consistent, evidence-based signal instead of a subjective read on each candidate.
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Best suited for teams that need to understand what's happening in their data before building anything predictive. Hire this profile when you need cohort analysis, funnel breakdowns, and dashboard-ready reporting that product and leadership teams can act on directly.
The right choice when you need to predict churn, demand, risk, or revenue ahead of time. This profile builds and validates statistical and machine learning models against real business outcomes, not just offline accuracy metrics that don't hold up in production.
Hire this profile when correlation isn't good enough and you need to know what actually moved a metric. They design A/B tests, control for confounders, and apply causal inference methods so that decisions are based on evidence, not coincidence.
Statistics Before Modeling
Most data science failures trace back to skipping the boring part, checking for sampling bias, validating that a metric actually measures what it claims to, or applying rigorous data preprocessing before training even begins. Our data scientists treat statistical rigor as the first step, not an afterthought caught in review.
Business Translation, Not Just Model Output
A forecasting model with 94% accuracy means nothing to a stakeholder who can't act on it. Our data scientists build the translation layer alongside the model itself, clear visualizations, plain-language readouts, and recommendations tied to specific decisions. The responsibility doesn't end at a Jupyter notebook; it continues through the conversation where a non-technical stakeholder decides what to do next.
Reproducible, Handoff-Ready Work
Analysis that only the original author can rerun is a liability, not an asset. Our data scientists version their code, document their assumptions, and structure notebooks and pipelines so another engineer can pick up the work without archaeology; that is one of the practical advantages of working with outsourced team instead of a single freelancer. When priorities shift or the engagement scales, the analysis doesn't have to be rebuilt from scratch.
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Structured analysis of existing data to surface patterns, quality issues, and opportunities, using the same Pandas and Seaborn based toolkit relied on day-to-day, before any model gets built.
Demand, revenue, churn, and risk forecasting models built in Python or R, validated against historical outcomes and tracked for drift.
Experiment design, sample size calculation, and statistical analysis for product and marketing tests that need to hold up to scrutiny.
Cohort analysis, segmentation, and funnel analysis that connect user behavior data to product and growth decisions.
Dashboards built in Tableau, Power BI, or custom visualization tooling that make ongoing metrics legible to non-technical stakeholders.
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Marketing mix modeling and causal inference methods that separate what actually drove a result from what merely correlated with it.
Feature engineering and data pipeline architecture that feeds clean, model-ready data into downstream analytics and ML systems.
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Independent review of existing models and analyses for statistical soundness before a business decision is made on them.
A Dedicated Data Scientist, Fully Embedded in Your Workflow
A full-time data scientist embedded in your team, working exclusively on your data, in your time zone, within your sprint cadence. Suited for ongoing analytics needs and evolving business questions.
Analytical Ownership
The scientist owns the full analytical lifecycle, from data validation and feature engineering through model building, evaluation, and stakeholder-ready reporting.
Sprint Integration
Works within your existing workflow, attends standups, contributes to planning, and delivers analysis on your team's cadence rather than on the side.
Context Continuity
Same scientist, same context, every sprint; assumptions, data quirks, and model history stay with one person instead of being relearned each time.
Toolchain Alignment
Works inside your existing data stack, notebooks, and BI tools, with no separate workflow to reconcile against your permanent team's setup.
Data Science Across Industries
Industry context changes which methods matter, a data scientist who has worked in your vertical understands the data quirks before the first analysis begins.
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Built for Every Stage of Your Data Maturity
Startups
Early-stage teams need a data scientist who can work with incomplete, messy data, answer the one or two questions that actually matter for the current stage, and set up tracking that doesn't need to be rebuilt as the product matures.
Mid-Size Businesses
Growing teams need data scientists who integrate with existing BI tools and data warehouses, extend analytics coverage without duplicating dashboards, and start moving from descriptive reporting into predictive and experimental work.
Enterprise Level Businesses
Enterprise data science requires working within governed data environments, documented model risk management practices, and cross-functional coordination with data engineering and compliance teams on regulated or business-critical analysis.
They have strong expertise in the latest technologies and provide excellent guidance in using them effectively.
CODE B launches the products quickly, and their solutions have excellent architecture and are scalable.
CODE B is proactive in coming up with solutions.
Aside from getting the job done, they’re able to provide their expertise and share their opinion.
They’re a very bright team that requires minimal levels of communication or time investment to be very effective.
Their constant communication was a key aspect of the success.
They completed the project within the timeline we gave them, and they did it within budget.
Had a great experience working with the team and in times of crisis, CODE B team was always there to support us.
The way that they have supported us by giving us one of their developers to work directly with our development team.
Our overall experience has been very positive.
They are friendly and reliable.
The ability to deliver on time impressed us the most.
They’re excellent at what they do and come up with solutions for various problems.
CODE B will work overtime to resolve issues, which is a difficult trait to find.
Code B’s communicative.
I’ve had a great experience working with CODE B
The main positive point of working with CODE B team is their analyzing skills.
They are receptive and try to adjust to meet our requirements.
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