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Data Science Consultancy · PhD Engineers Since 2013

Data Science Consulting & Development Services

Q: What Is the Difference Between Data Science Consulting and Data Science Development Services?

Data science consulting decides what model, analysis or data product you need, the problem framing, exploratory data analysis, feasibility assessment and roadmap. Data science development services are the engineering work to build, integrate and operate the predictive analytics, forecasting models, anomaly detectors and data pipelines that result. Most production data science engagements need both. Winder.AI delivers them as one engagement, so the data scientists writing the strategy are the same engineers writing the production code. That removes the handover gap where most data science projects stall in a notebook.

The specialist data science consultancy that has delivered predictive analytics, forecasting, anomaly detection and production data systems since 2013. Data science consulting, development services and data infrastructure for enterprise teams, by PhD engineers. Multi-cloud and regulated industries.

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Talk to the data scientists

Tell us about your data science consulting, predictive analytics, anomaly detection or data infrastructure need, and we'll tailor an approach. Typically two to four weeks from first call to kick-off.

2013

Specialist data science consultancy incorporated in 2013, over a decade of predictive analytics and production data delivery.

Shell

enterprise data quality and analytics platform delivered for Shell, one of many data science case studies.

Grafana · Pachyderm

data infrastructure and data science work delivered for category-leading data and observability companies.

4×

cloud-agnostic delivery: AWS, Azure, GCP and on-prem, including air-gapped environments.

What you get

What a data science consultancy actually delivers

A data science consultancy turns raw data into reliable business outcomes. That means exploratory data analysis, predictive analytics and forecasting, anomaly detection, recommendation systems, the data lake and pipeline infrastructure that supports them, and the production engineering that keeps them running. Winder.AI delivers end-to-end data science consulting and development services as one engagement, scoping, modelling, deployment and operations, by PhD engineers who have shipped predictive analytics and production data systems for Google, Shell, Grafana, Pachyderm and Ofcom since 2013. No reports without the production code behind them.

How we compare

How data science consulting firms compare

Provider type	What they deliver	Best for	Main weakness
Big-4 / global IT consultancy	Data strategy decks, roadmaps, large delivery teams	Multi-year data transformation programmes	Hands-on data science and modelling offshored or thinly staffed
Generalist analytics consultancy	BI dashboards, reporting, descriptive analytics	Reporting layers and executive dashboards	Light on predictive modelling, ML and production engineering
Offshore data science shop	Junior data scientists, notebook-grade deliverables	Low-cost exploratory work with internal oversight	Shallow seniority, weak on production engineering and governance
Cloud vendor professional services	Reference implementations on the vendor's data stack	Standardising on a single cloud data platform	Lock-in by design, weak on multi-cloud or open-source
Specialist data science consultancy (Winder.AI)	Data science consulting, predictive analytics, anomaly detection, data infrastructure and production ML, delivered by PhD engineers	Enterprises that need production-grade data science, multi-cloud, in regulated industries	Boutique scale, not designed for 100-seat staff augmentation

From strategy to production

Data science consulting, development services and production ML

Winder.AI is the data science consulting partner for organisations that need predictive analytics to drive real business outcomes, not slide decks. Our data science services span strategy and exploratory data analysis, predictive modelling and development, and the production data infrastructure that operationalises the result, the full lifecycle, by PhD engineers who have delivered data science at enterprise scale since 2013.

Data Science Consulting & Strategy

Data strategy, exploratory data analysis, problem framing, feasibility and roadmap. We isolate the questions that data can answer profitably, validate the data is fit for purpose, and recommend the right modelling approach for your scale, cloud strategy and team. Part of our broader AI consulting practice, with continuity through to development and production.

Data Science Development & Predictive Analytics

Hands-on data science engineering: forecasting models, classification systems, anomaly detection, recommendation engines and bespoke predictive analytics, built on Python, pandas, scikit-learn, PyTorch and TensorFlow. We are engineers first, which means production code, not just notebooks, with integration into your operational systems from day one.

Production ML & Data Infrastructure

Data lake architecture, data pipelines and the production engineering that takes a model from notebook to live service. Extends naturally into our MLOps consulting practice for monitoring, retraining and governance, so your data science doesn’t end at a hand-off.

Winder.AI are top-tier experts in the machine learning and MLOps space. They have an incredible breadth of experience and work with clients ranging from small startups to Fortune 100 enterprises, so the range of challenges they can tackle is vast.

Joey Zwicker

co-founder and CTO of Pachyderm

Why hire a data science consultancy

The data science consulting partner enterprises choose

Over a decade of predictive analytics and production data science across regulated industries, multi-cloud delivery and a senior PhD engineering bench, not a sales layer.

12+ Years of Production Data Science

Delivering data science since 2013, one of the longest-running specialist data science practices in the UK. We know which approaches survive contact with production data and which collapse on first deployment.

PhD Engineers, Multi-Cloud by Default

Data science engagements delivered by PhD-level engineers across AWS, Azure, GCP and on-prem, including air-gapped environments. SOC 2, GDPR, HIPAA and EU AI Act-aware engagements as standard, the regulated default, not a premium add-on.

Senior Data Scientists, No Sales Layer

You talk to the data scientists who will do the work. No offshore handover, no junior squad behind a senior pitch. The team that scopes your data science engagement is the team that builds it.

Trusted Worldwide

Trusted by global organisations for data science

Predictive analytics, forecasting, anomaly detection and data infrastructure delivered across finance, energy, technology, media and government.

Data Science Solutions

Data science solutions and predictive analytics services

Real-world data science is the difference between a clean dataset and a business outcome. Winder.AI delivers data science solutions as discrete service lines, from exploratory analysis through to production forecasting and anomaly detection, so you can engage at any point in your data science maturity:

Exploratory Data Analysis (EDA)

A focused exploratory data analysis engagement to validate hypotheses, surface hidden patterns and confirm a dataset is fit for predictive modelling. EDA is the first step of every serious data science programme, and the cheapest way to find out whether your data is the problem.

Predictive Analytics & Forecasting

Custom forecasting and predictive analytics for demand, financial planning, energy, capacity and operational metrics. We build with classical methods (ARIMA, Prophet, state-space models) and deep learning where the data justifies it, and deploy through our MLOps services.

Anomaly Detection

Anomaly detection for fraud, security, manufacturing, observability and operational risk. Unsupervised, supervised and hybrid approaches across time-series, transactional and high-dimensional data, deployed into production monitoring so alerts reach the right team in time.

Recommendation Systems

Personalisation and recommendation systems for content, commerce and operational decision support. We design the modelling approach, the feature pipeline and the production serving layer, and integrate the result into your existing application or platform.

Data Lake & Data Infrastructure

Data lake architecture, data warehousing and the pipelines that consolidate your data assets. Our data infrastructure consulting ensures your platform is ready for analytics, machine learning and future growth across Snowflake, Databricks, BigQuery, Airflow and dbt.

Data Science POCs

A time-boxed 8-to-12-week proof of concept that de-risks a larger investment. Scoping, exploratory data analysis, baseline modelling and an honest go or no-go assessment. Move straight into a production build with our AI product development team when the answer is go.

Data Science Technical Capabilities

Data science expertise, end to end

We cover the full data science stack across the major frameworks and platforms, from Python, pandas and scikit-learn to PyTorch and TensorFlow, and the operational disciplines that turn predictive analytics into reliable production systems:

Python, pandas & scikit-learn

The everyday data science stack. Production-grade Python codebases, vectorised pandas pipelines and scikit-learn modelling for classification, regression, clustering and feature engineering at scale.

PyTorch & TensorFlow

Deep learning across PyTorch and TensorFlow for sequence modelling, computer vision, structured-data deep learning and custom architectures. Trained on GPU clusters and shipped into production through our MLOps practice.

Time-series & Signal Processing

Time-series analysis, signal processing and event-stream analytics for sensors, finance, energy and operational telemetry. The right transform, decomposition and feature design is half the modelling battle.

Statistical Modelling

Classical statistical modelling for inference, A/B testing, causal analysis and uncertainty quantification. Knowing when a regression beats a neural network is a senior data science skill, and we keep it sharp.

Forecasting (Prophet, ARIMA, deep learning)

Forecasting across the full toolkit, ARIMA and state-space models, Prophet, gradient-boosted trees, and recurrent or transformer architectures. We pick the smallest model that hits the business outcome, then deploy it for real.

Anomaly Detection

Anomaly detection across unsupervised, supervised and hybrid approaches: isolation forests, autoencoders, density estimation and sequence models, calibrated against the operational cost of false positives.

Snowflake, Databricks & BigQuery

Production data science on Snowflake, Databricks and BigQuery, including feature stores, in-warehouse modelling and integration with downstream serving layers. Stack-agnostic by design.

Airflow, dbt & Data Pipelines

Orchestration with Airflow, transformations with dbt, event streams with Kafka, and the data engineering that turns ad-hoc analysis into a repeatable production pipeline.

Your data science stack questions, answered Framework-agnostic by design, we fit your existing data stack or recommend the best one for the problem.

Which modelling framework should we use?

Framework-agnostic by design

We pick the framework that fits your problem, team and data, not the one we are most familiar with. Classical, deep learning or hybrid, the model fits the problem.

Pythonpandasscikit-learnPyTorchTensorFlowXGBoostProphetStatsmodels

Where will the data science workload run?

Deployment, your way

Production data science on your cloud, hybrid or fully air-gapped on-prem. Regulated and sovereign environments fully supported.

AWSAzureGCPOn-premKubernetesDockerAir-gapped

How does this fit our existing data stack?

Plug into your data stack

We connect data science pipelines to your warehouses, lakes and event streams. No “send us a CSV”, we integrate with your real systems.

SnowflakeDatabricksBigQueryS3PostgresKafkaAirflowdbt

Will this pass security and compliance review?

Security & compliance ready

Built for regulated environments. SOC 2, GDPR and HIPAA-ready engagements with full audit trails, model lineage and data-residency controls.

SOC 2GDPRHIPAAEU AI ActData residencyAudit logsSSO

Selected Case Studies

Some of our most recent work for our clients. You can find more in our portfolio.

2020Case study

Improving Data Science Strategy at Neste

Winder.AI helped Neste develop their data science strategy to nudge their data scientists to produce more secure, more robust, production ready products. The results of this work were:

A unified company-wide data science strategy
Simplified product development - “just follow the process”
More robust, more secure products
Decreased to-market time

Our Client

Neste is an energy company that focuses on renewables. The efficiency and optimization savings that machine learning, artificial intelligence and data science can provide play a key role in their strategy.

2019Case study

Google Releases AI Platform with help from Winder.AI

At their Cloud’s Next 19 conference, Google has announced the launch of an expanded AI platform. For a number of years Google has been expanding it’s portfolio to compete with AI products from Azure and AWS. But this is the first time that the platform can be considered “end-to-end”.

2018Case study

Using Data Science to block hackers

Executive Summary

Winder.AI was engaged by Bitsensor to research and implement Data Science algorithms that could automate the detection and classification of web attackers. After gathering data, researching a Machine Learning solution and implementing Cloud-Native software, we delivered three new features:

Tool classification - detect which automated tools were being used to perform the attack
Attacker grouping - provide the capability of detecting distributed attacks by the same attacker
Killchain classification - establish the phase of an attack (e.g. reconnaissance, exploitation, etc.)

Client

Bitsensor is a startup in the Netherlands that specializes in protecting public-facing websites and applications. They distribute their web-application firewall product to a range of customers throughout Europe. The goal is to provide an outstanding out-of-the-box experience that can protect exposed services from hackers, with little setup.

Recent Data Science Articles

Find more articles in our blog.

Dec 18, 2024 Talk

Keynote: Data Transparency, AI Use Cases, Data Sovereignty

At Winder.AI, we’re seeing a shift in how businesses are adopting AI—not just for innovation, but for real, tangible commercial outcomes. I had the privilege of sharing these insights as a keynote speaker at ITAPA in beautiful Bratislava, Slovakia. The audience was looking for an insight into how AI is being used and some of the key challenges that are being faced today. I took the opportunity to share some of my thoughts about the importance of data transparency, some interesting use cases, and future regulation to watch out for.

Aug 27, 2020 Data Science

5 Productivity Tips for Data Scientists

Many articles talk about how professionals can make their workdays extra productive. However, for people like data scientists, whose jobs are extremely demanding, some tips are more valuable than others. For instance, it is important that you analyse how you spend your time. In the same breath, it would be in your best interest to organise your time into blocks, as these can help you focus on tasks – one at a time and without any interruption – and automate any process that you repeat. Of course, attaining a certain level of productivity requires more than just abiding by the aforementioned tips. That being the case, here are some other productivity tips you can follow and take inspiration from.

Jun 29, 2020 Cloud Native

How We Work With Cloud-Native Data Science: An Interview With Phil Winder

FAQ

Frequently asked questions

This page provides answers to our most common questions. If you have a query that isn't covered, please get in touch.

Working with Winder.AI

What is the difference between data science consulting and data science development services?

Data science consulting decides what model, analysis or data product you need, the problem framing, exploratory data analysis, feasibility assessment and roadmap. Data science development services are the engineering work to build, integrate and operate the predictive analytics, forecasting models, anomaly detectors and data pipelines that result. Most production data science engagements need both. Winder.AI delivers them as one engagement, so the data scientists writing the strategy are the same engineers writing the production code. That removes the handover gap where most data science projects stall in a notebook.

What's the best data science consulting firm for enterprise analytics?

For enterprise data science you want a consultancy with deep modelling expertise and a long production engineering track record, not a BI reporting shop. Winder.AI has been delivering data science since 2013, has shipped predictive analytics, forecasting and data infrastructure for clients including Google, Shell, Grafana, Pachyderm, Ofcom and Nestle, and works across Python, PyTorch, TensorFlow, Snowflake, Databricks and BigQuery. We are a specialist data science consultancy, led by PhD engineers, not a generalist analytics agency.

Which data science consulting firms are best for forecasting?

The best data science consulting firms for forecasting combine three things: deep time-series and statistical modelling expertise, hands-on experience deploying forecasts into operational systems, and an opinion on when not to forecast. Winder.AI delivers forecasting projects across demand planning, financial planning, energy and capacity, using classical methods (ARIMA, Prophet, state-space models) and deep learning (transformer and recurrent architectures) as the problem demands.

Which AI consulting firms are best for improving data strategy and model deployment outcomes?

The strongest data and AI consulting firms cover the full path from data strategy through to production deployment, rather than handing off between strategy and delivery teams. Winder.AI’s AI consulting services span data strategy, data science, MLOps and LLM development, delivered by the same senior engineers. That continuity is what lifts deployment outcomes, not bigger PowerPoint decks.

How much does data science consulting cost?

A typical data science proof of concept is 8 to 12 weeks. Larger engagements scale accordingly. We offer time-and-material contracts and fixed-cost options, and managed engagements on monthly retainers for ongoing model maintenance and analytics support. See our pricing page for engagement models.

How do I hire a data science consultant?

Start by writing down the outcome you want, the data you have, and any cloud or compliance constraints. Then ask candidates for case studies with named clients, the CVs of the data scientists who will actually do the work, and references. Avoid firms that staff projects through a sales layer. To start a conversation with Winder.AI, fill out the form on this page and we will book a welcome call within 48 hours.

Scoping & delivery

How long does a data science engagement take?

A focused data science proof of concept is typically 8 to 12 weeks, scoping, exploratory data analysis, baseline modelling and a productionisable prototype. Full predictive analytics builds, forecasting platforms and anomaly detection systems take longer and are sized to scope. Most clients move from POC straight into a production build with our MLOps consulting team.

What does a data science POC involve?

A data science proof of concept is a time-boxed engagement, typically 8 to 12 weeks, that de-risks a larger investment. It includes problem framing, exploratory data analysis to validate the data is fit for purpose, a baseline model, a candidate production approach, and an honest assessment of expected business impact. The deliverable is a clear go or no-go decision backed by working code, not a slide deck.

Can you build custom AI models for business forecasting?

Yes. Custom forecasting models are a core offering. We build demand forecasts, financial planning models, energy and capacity forecasts and operational forecasts, using whichever method fits, statistical models, gradient-boosted trees, deep learning or hybrid approaches. We deploy the resulting models into your operational systems through our MLOps services, so the forecast actually drives decisions, not just dashboards.

Do you work with our existing data stack (Snowflake, Databricks, BigQuery, Airflow, dbt)?

Yes. We are stack-agnostic by design and have delivered production data science on all of the major data platforms. If you have already standardised on Snowflake, Databricks, BigQuery, Airflow or dbt we fit into that. If you are still selecting, we recommend the right platform for your scale, cloud strategy and team structure.

How quickly can you start a data science engagement?

Typically two to four weeks from first call to kick-off. Discovery and scoping usually take one to two weeks, contracting another one to two weeks. Urgent engagements can start inside a week. Get in touch early even if your timeline is flexible, as our calendar fills four to eight weeks ahead.

Do you work in regulated environments (finance, insurance, healthcare)?

Yes. A substantial part of our data science work is for regulated clients, including UK financial services. We run engagements compatible with SOC 2, GDPR, HIPAA, the EU AI Act and on-prem or air-gapped deployments, with full audit trails and data-residency controls.

Data science, explained

What is data science?

Data science is the discipline of extracting reliable, actionable insight from data, using a combination of statistics, machine learning, software engineering and domain expertise. In practice, a data science engagement covers exploratory analysis, predictive modelling (forecasting, classification, anomaly detection, recommendation), the data infrastructure that supports those models, and the production engineering that operationalises them.

What is the difference between data analysis, data science and machine learning?

Data analysis focuses on examining existing data to identify patterns, trends and insights, often through statistics and visualisation. Data science is broader, it includes data analysis but also predictive modelling, machine learning and building systems that learn from data. Machine learning is one toolkit within data science, focused on models that improve with more data. Most production projects need all three, and a good data science consultancy is fluent in each.

When should we use predictive analytics instead of descriptive analytics?

Use descriptive analytics (BI, dashboards, reporting) when the question is “what happened?” and the decision is human. Use predictive analytics when the question is “what will happen next, and what should we do about it?”, and especially when the decision is operational or repeated at scale. Forecasting demand, scoring leads, detecting anomalies and recommending content are all problems where predictive analytics earns its keep over a dashboard.

What is a data science proof of concept (POC) versus a full project?

A data science POC is a time-boxed feasibility engagement, typically 8 to 12 weeks, that proves a model can hit a defined business outcome on your real data. A full project takes the validated approach and engineers it into a production system: data pipelines, monitoring, retraining, integration with operational tools. We deliver both, and the same team typically takes a POC through to production with our AI product development and MLOps services.

What data quality do we need before starting a data science project?

You need enough data of high enough quality to answer the question you’re asking. “Enough” depends on the problem, anomaly detection on millions of transactions is different from forecasting monthly revenue. We run a short data discovery as the first step of any engagement to confirm the data is viable. If it isn’t, we say so, and we recommend a data infrastructure or data engineering project first.

How do I choose a data science consulting firm?

Look for deep technical seniority (PhD-level or equivalent), a long delivery track record with named clients, production engineering experience (not just notebooks), comfort across multiple clouds and frameworks, and a clear opinion when your problem is not a data science problem. Winder.AI has delivered data science and AI solutions since 2013 for clients including Google, Grafana, Shell, Pachyderm, Ofcom and Nestle.

What do AI consulting firms typically provide for enterprise data, governance and automation programmes?

A capable AI consulting firm provides data strategy, data science and predictive analytics development, MLOps and production engineering, governance and audit frameworks, and the change-management support to embed the result. Winder.AI delivers these as one continuous practice across AI consulting, data science, MLOps and LLM development, so enterprise programmes do not stall between phases.

Get Started

Start your data science engagement

Whether you need a data science consulting engagement, a predictive analytics or forecasting build, an anomaly detection system, a data lake and infrastructure project, or a focused proof of concept, talk to the team that has been delivering data science since 2013.

You'll talk to PhD data scientists, never a sales layer
Welcome call booked within 48 hours
Typical data science POC: 8 to 12 weeks

Ready when you are

Send us a brief and book a welcome call within 48 hours.

Talk to the data scientists

Need a data science consultancy that ships production analytics? Start your data science engagement