section services -- data science
Data science services for production-ready models.
Predictive modeling, machine learning development, and MLOps that take a forecast from a notebook to a system your team actually relies on.
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definition
What are data science services?
Data science services build statistical models and machine learning systems that predict what happens next, using your own historical data: demand forecasts, churn risk, fraud detection, and pricing models. The deliverable is a model that runs in production and gets monitored, not a one-time analysis in a notebook.
key takeaways
A data science project that never leaves a notebook has not shipped, no matter how accurate the model is - production deployment is the actual deliverable.
The US data science platform market is projected to reach $44.14 billion by 2025 (Market.us, 2025), and demand keeps outpacing supply.
Data scientist employment is projected to grow 34% from 2024 to 2034, far faster than the average occupation, at a median wage of $112,590 (U.S. Bureau of Labor Statistics, 2025).
Model accuracy decays over time as real-world data drifts from what a model was trained on, which is why MLOps monitoring matters as much as the initial build.
// data science vs. business intelligence
What separates data science from business intelligence? BI tells you what happened last quarter. Data science tells you what is likely to happen next quarter, and how confident to be in that prediction. A BI dashboard showing last month's churn rate is reporting; a model predicting which customers are likely to churn next month, before they do, is data science. Most mature data platforms run both together.
capabilities - seven
What our data science team does.
Seven capabilities, one goal: a model that runs in production, not a one-time analysis.
- 01
Predictive modeling & forecasting
Models trained on your historical data to forecast demand, churn, and risk, so decisions get made ahead of the event instead of in reaction to it.
- 02
Data engineering & pipelines
Clean, reliable pipelines feeding your models, because a model is only as good as the data actually reaching it.
- 03
Statistical analysis & experimentation
A/B testing frameworks and statistical rigor applied to product and pricing decisions, not gut-feel calls dressed up as data.
- 04
Machine learning model development
Classical ML and deep learning models built and evaluated against your actual business metric, not a leaderboard score that never ships.
- 05
MLOps & deployment
Models packaged, deployed, and monitored in production, with drift detection and a retraining schedule instead of a notebook that never leaves someone's laptop.
- 06
Data strategy & maturity assessment
An honest read on whether your data can support the model you want built, before you spend budget finding out the hard way.
- 07
Ongoing model monitoring
Accuracy tracked in production, with alerts when a model drifts and a retraining cadence that keeps it useful past month one.
method
How does a data science engagement actually work?
A Syndell data science engagement runs five stages: data maturity assessment, pipeline setup, model development, deployment, and ongoing monitoring. Every stage ships something you can evaluate, not a progress update.
- 01
Data maturity assessment
// outcome
-> an honest read on whether your data can support the model you want
- 02
Data engineering & pipeline setup
// outcome
-> clean, reliable data flowing into the model consistently
- 03
Model development & evaluation
// outcome
-> a model evaluated against your actual business metric, not a leaderboard score
- 04
Deployment & MLOps
// outcome
-> the model live in production with monitoring and a retraining pipeline
- 05
Ongoing monitoring
// outcome
-> accuracy tracked over time, with drift caught before it costs you a decision
technologies our data science team builds with
- Python
- R
- TensorFlow
- PyTorch
- Databricks
- AWS SageMaker
-- data science, in numbers --
projected growth in data scientist employment from 2024 to 2034, far faster than the average occupation
src - U.S. Bureau of Labor Statistics, 2025median annual wage for data scientists in the United States as of May 2024
src - U.S. Bureau of Labor Statistics, 2025the honest part
Why most data science projects never ship.
The most common failure in data science is not a bad model. It is a good model that stays in a Jupyter notebook because nobody scoped how it would reach production, get monitored, or get retrained once the world it was trained on starts to shift.
MLOps is the unglamorous half of the work: packaging a model so it can be deployed reliably, wiring up monitoring that catches accuracy drift before a bad prediction reaches a customer, and scheduling retraining so the model stays useful past the first quarter. We scope that half of the project from day one instead of treating it as an afterthought once the model itself is "done."
I keep saying that the sexy job in the next 10 years will be statisticians, and I'm not kidding.
- Hal Varian
Google's Chief Economist -- FlowingData interview, 2009
Data is the new oil.
- Clive Humby
Mathematician -- Association of National Advertisers conference, 2006
engagement
Which engagement model fits you?
| Model sprint | Production ML build | Dedicated data science team | |
|---|---|---|---|
| Best for | Testing whether a single forecasting or classification model is viable | Taking a validated model from notebook to production | Ongoing modeling work across a growing product roadmap |
| You get | A data audit, one trained model, and an evaluation report | Deployment pipeline, monitoring, drift detection, and a retraining schedule | Embedded data scientists and engineers who ship continuously |
| Timeline | Weeks | Months, scope-dependent | Quarterly, renews |
| Team | Data scientist + data engineer | Full data science and MLOps squad | Your composition, our bench |
We don't publish a pricing table here on purpose - data readiness and model complexity decide the real cost, and an audit gets you a number a generic rate card never could. Tell us what you're trying to predict.
frequently - asked
Five questions,
straight answers.
01What is included in data science services?
Data engineering and pipeline setup, statistical analysis, predictive model development, machine learning deployment, and MLOps for ongoing monitoring. You can start with a single forecasting model or a full data science program.
02What is the difference between data science and business intelligence?
BI reports what already happened, using dashboards built on historical data. Data science predicts what happens next, using statistical models and machine learning trained on that same data. Most teams need both, and the two practices share a data foundation.
03Do we need a data scientist on staff, or can this be outsourced?
Most businesses outsource data science until the workload justifies a full-time hire. An external team can build and deploy a production model faster than a single in-house hire ramping up, and you can scale the engagement up or down as the roadmap changes.
04How do you take a model from a notebook to production?
Through MLOps: packaging the model, setting up a deployment pipeline, monitoring for drift, and retraining on a schedule. A model that only runs in a data scientist's notebook has not shipped, regardless of how accurate it is.
05How much do data science services cost?
It depends on data readiness and model complexity. A forecasting model on clean, existing data costs a fraction of a full MLOps pipeline built from scratch. Book a call and you'll get a concrete estimate against your actual data.
the practice
Explore our data and AI practice.
Data science rarely runs alone. Most engagements pair it with the rest of the data and AI practice:
- Business intelligence
the dashboards and reporting layer that surface what your models predict
- Data visualization
making model output and forecasts legible to people who are not data scientists
- AI and machine learning development
production ML systems and MLOps for teams scaling past a single model
- AI consulting
use-case triage and strategy for teams deciding where a model actually pays off
- Hire a dedicated data scientist
bring a data scientist directly onto your team instead of running a separate project
-- next issue - your first model --
Start your data science project.
Tell us what you're trying to predict - we'll tell you honestly whether your data can support it yet.
