AI-Assisted Feature Engineering
AI tools analyze your raw data and suggest transformations, derived features, and encoding strategies that improve model performance without manual exploration.
Hire Data Scientists
Hire Data Scientists Who Turn Your Data into Decisions
Our data scientists build predictive models, analytics pipelines, and data-driven systems that generate measurable business value. Not dashboards that nobody checks, but actionable insights integrated into your workflows.
Data Science specialists
AI-accelerated delivery
Start within a week
150+Happy Clients
12+Years Delivery
13+Countries Served
24hResponse Window
AI-Native Delivery
Data Science, accelerated with AI
Automated Model Selection
AI benchmarks multiple model architectures against your data, identifying the best algorithm and hyperparameters for your specific prediction task.
Data Quality Monitoring
AI continuously monitors incoming data for drift, outliers, and quality issues that degrade model accuracy, triggering alerts before predictions become unreliable.
Rapid Experimentation
AI-accelerated experiment tracking, notebook management, and pipeline orchestration mean hypotheses are tested and validated in days rather than weeks.
Every engineer at Quantum Infoway uses AI as a core part of their engineering workflow. This is not about replacing developers with AI — it is about making experienced developers significantly more productive.
What Our Data Science Scientists Build
Data Science systems Quantum Infoway talent ships
Predictive Analytics
Forecasting models for revenue, demand, churn, and operational metrics. Time series analysis with confidence intervals and scenario modeling.
Machine Learning Models
Classification, regression, clustering, and recommendation systems trained on your business data. From customer segmentation to fraud detection.
Data Pipeline Engineering
ETL pipelines that extract data from multiple sources, transform it for analysis, and load it into data warehouses. Scheduled, monitored, and fault-tolerant.
Business Intelligence & Dashboards
Interactive dashboards in Tableau, Power BI, or custom tools that surface the metrics that matter. Connected to live data sources with automated refresh.
NLP & Text Analytics
Sentiment analysis, topic modeling, document classification, and entity extraction from customer reviews, support tickets, and social media data.
A/B Testing & Experimentation
Statistical experiment design, sample size calculation, and rigorous analysis for product decisions. Bayesian and frequentist approaches based on your needs.
Vetting Pipeline
How engineers earn a spot on our team
Every engineer goes through the same four-stage funnel. Most do not make it past stage two.
30,000+
Applications received each year
Around 15 open roles at any time, each receiving roughly 200 applications per month through LinkedIn, AngelList, employee referrals, and outbound headhunting across India. India produces 1.5 million CS graduates a year, so sourcing is not the moat. Filtering is. Most applications are auto-filtered for stack relevance, years of experience, English, and time-zone overlap before any human reviews them.
Pass live coding + system design
Two-stage technical interview: a 90-minute live coding exercise tailored to the candidate’s primary stack, then a system-design discussion grounded in real production scenarios.
Pass project deep-dive + background verification
We ask candidates to walk through a recent production project: architecture decisions, what they would change in hindsight. We also run a background verification check through a third-party BGV agency to confirm prior employment, education, and identity.
Complete 2-week shadow onboarding
A senior Quantum Infoway engineer shadows new hires for two weeks on an internal project. We watch how they respond to feedback, ask for help, and ship code. Fast learners stay.
Join the active team
A dedicated team of engineers makes up the active Quantum Infoway bench across stacks at any given time. New roles open as engagements complete or grow.
How It Works
From first conversation to a developer shipping code on your project
The process is designed to be fast, transparent, and low-risk — whether you need a data scientist, a managed team, or a fixed-scope build.
Share your goals
Tell us about the product, stack, timezone overlap, and whether you need a dedicated hire, managed team, or fixed-scope build.
Meet shortlisted engineers
We match vetted candidates to your requirements and share profiles, sample work, and availability within a few business days.
Interview & select
You interview directly. If the fit is not right, we continue until you are confident — no pressure to lock in early.
Start within a week
Onboarding into your tools and channels is typically complete within 14 business days. Engagements run monthly with flexible scale.
Engagement Models
How you can hire Data Science talent
Dedicated Developer
A Data Science developer works exclusively on your project, integrated with your team’s tools and workflows.
Best for: Ongoing Data Science ownership and iteration
Managed Team
We assemble and manage a Data Science team with a tech lead, handling delivery end-to-end against your requirements.
Best for: Building a Data Science product end-to-end with a lead
Project-Based
Fixed scope, timeline, and budget. We deliver the project and hand off the codebase with documentation.
Best for: New builds, rebuilds, and integration work- Start within a week
- Flexible scale-up / scale-down
- No long-term lock-in
- Dedicated technical lead
Tell us which model fits — we will recommend the leanest path.
Quantum Infoway scopes every engagement before any quote becomes final.
Pricing
Transparent Data Science developer rates, published
Hourly dedicated
$25 to $50 per hour
Dedicated Data Science developers, by seniority
Fixed-scope builds
From $15,000
Focused builds with clear deliverables and documentation
US specialists typically bill $150 to $300 per hour for comparable Data Science scope. Every engagement is scoped individually before any number becomes a quote.
Get a scoped quote
Our Work
Data Science products we have shipped
Integrated three AI features that reduced compliance effort by 97% for 15,000+ learners
- Brain AI knowledge base answering student and staff queries instantly
- English Master adaptive language module with pronunciation feedback
- Two-way live translation for multilingual student populations
- Compliance document generation for audit-ready reports
97%Compliance effort reduction
25%Faster English acquisition
15,000+Learners supported
Democratized data access with a natural language analytics bot cutting reporting time by 50%
- Users query marketplace data in plain English without SQL
- Results as interactive tables, summaries, or visualizations
- Cross-table queries spanning multiple data sources
- Query history and saved reports for recurring questions
50%Faster reporting
NLQNatural language query
Self-serveDemocratized data access
Increased booking conversion by 20% with AI-powered property matching
- Recommendation engine analyzing guest preferences, booking history, and behavioral signals
- Personalized property suggestions from 1,000+ listings in real time
- AI suggestions integrated into search and booking without disrupting UX
- A/B testing framework to measure conversion impact of recommendation placements
60%Higher booking conversion
35%More property views
1,000+Listings matched
Your Data, Working for Your Business
Quantum Infoway matches you with vetted data scientists who care about production quality and clear communication.
Talk to an ExpertFAQ
Hiring data scientists — FAQs
How much does it cost to hire a data scientist?
Our data scientist rates run $25 to $50 per hour depending on seniority, and focused fixed scope builds typically start around $15,000. US specialists bill $150 to $300 per hour for comparable work. Most clients choose a monthly dedicated model with a 30 day notice period and no long term lock in.
How does the engagement work once I hire?
Your developer works as an extension of your tech team, with direct communication in your channels and hours that overlap yours. Vetted candidates complete onboarding within 14 business days. If a developer underperforms, we replace them, and engagements run monthly with a 30 day cancellation notice.
How quickly can you provide a Data Scientist?
We can match you with a vetted Data Scientist within a week. Our team includes pre-screened engineers with production experience in Data Science, so we skip the lengthy recruitment cycle and get straight to onboarding.
What engagement models do you offer for Data Science development?
Three options: dedicated developers who work exclusively on your project, a managed team where we handle delivery end-to-end, or a project-based engagement with fixed scope and timeline. All models include a technical lead and regular progress updates.
How do you vet your Data Science developers?
Every data scientist is tested on real analysis work: a feature-engineering and model-selection exercise, an experiment-design and evaluation task (train/test discipline, metrics, leakage checks), a SQL and data-wrangling round, and a trial project. We also check how they communicate findings to non-technical stakeholders and work with a distributed team.
Can I interview the developer before starting?
Yes. We share detailed profiles including relevant project experience, then arrange a technical interview so you can assess fit before committing. If the match is not right, we provide alternatives at no cost.
What happens if the developer is not the right fit?
We offer a replacement guarantee. If the developer does not meet expectations within the first two weeks, we reassign and provide a replacement with no additional charges or delays to your project timeline.
What is the difference between a data scientist and a data engineer?
Data engineers build the infrastructure: pipelines, databases, and data warehouses that collect and organize data. Data scientists analyze that data: building models, running experiments, and generating insights. Our team includes both skill sets, so we can build the full stack from data collection to production model deployment.
How much data do we need before data science is useful?
It depends on the problem. For simple analytics and dashboards, even modest datasets provide value. Predictive models typically need hundreds to thousands of relevant records. For deep learning, you need more. During discovery, we assess your data volume, quality, and relevance to determine what is feasible and what would benefit from additional data collection.
Prefer a team?
Beyond dedicated developers, our teams deliver complete products
Insights