88% of enterprise data science pilots die before production. We are a data science
services company offering end-to-end data science services from data collection and cleaning to advanced
analytics, machine learning models, and actionable visualizations. Whether you’re a startup trying to make
sense of your numbers or an enterprise looking to scale data operations, we build solutions that fit your
goals.
Let’s talk about what your data can do for you. Contact us today.
Most data science services fail to deliver business value. Traditional services hand you a static slide deck or a dashboard that quickly gets abandoned. But as a leading data science services provider, we skip the shelfware and take the full responsibility of building, deploying, and maintaining your models directly into your daily operations.
Data science services involve extracting actionable insights from raw data using advanced analytics, statistics, and machine learning. It turns your data into deployed modes, decision systems, and dashboards that pay back. It includes data strategy, use-case prioritization, predictive modeling, machine learning, MLOps, data engineering, and post-delivery drift monitoring, all in a single SOW.
Most buyers confuse data science with data analytics, ML engineering, BI, and AI, but these disciplines solve entirely different problems and require different first hires and toolsets. The trap arises from the gap between strategy decks and production code, resulting in 88% pilot mortality and an average cost of $4.2M for abandoned projects.
We’re here to solve this problem, offering strategy + production-grade build + retraining SLA under one engagement. Our architects stay involved in your project, collaborating directly with the team operating the system to ensure it works in the real world.
If you only need a slide deck, hire McKinsey. If you only need a Kaggle model, post on Upwork. If you need someone who will defend the model in the boardroom, debug the feature store at 2 AM, and stay accountable when it drifts, that’s us.
Data science consulting services enable businesses to leverage their raw data to inform strategic decisions, optimize operations, and solve complex business problems. It is different from implementation services; data science consulting services are what board-level buyers, CTOs, and Heads of data start with when they need to know what to build, what to buy, and what to kill.
A clear, strategic 12-month data plan with a prioritized use-case backlog, clear recommendations on whether you should build custom-built software or purchase existing tools, and a quarterly delivery plan. We deliver a concise memo approved by the CFO, not a 40-page deck nobody reads.
The 7-axis scorecard grades your data on data quality, volume, freshness, governance, latency, and ROI clarity. The memo takes the complex scores and turns them into a go/no-go on whether you’re ready for the pilot.
Workshop-driven discovery of 12–30 candidate use cases scored on impact and feasibility, sequenced into a delivery plan.
Independent review of warehouse, lakehouse, pipelines, feature store, governance, and MLOps backbone. Risk register + remediation plan with priorities and cost ranges.
Second-opinion validation of an existing model accuracy, drift, fairness, governance, and SR 11-7 readiness. We say what works and what doesn’t in writing, with the math shown.
Principal-level fractional advisory at 8–16 hr/month or convert the embedded team to your payroll. Reviews, vendor escalations, board prep.
Not sure where to start? The Data Readiness Diagnostic below answers 5 questions to tell you whether you need consulting, a pilot, or just better data engineering.
Open the DiagnosticTYPICAL BUYER: $30K–$120K
TYPICAL BUYER: $80K–$200K
TYPICAL BUYER: $85K–$900K
There are four ways to get data science capability into your business. Here’s an honest comparison to match when each option makes sense, including data science as a service (DSaaS) off-the-shelf platforms like DataRobot, H2O, Azure ML Studio, and AWS SageMaker AutoML, which we’ll name and recommend when they actually fit.
| Dimension | In-house team | Big-4 consulting (Deloitte, Accenture, EY) | Trango Tech | DSaaS (DataRobot, H2O, Azure ML) |
|---|---|---|---|---|
| Time to first deployed model | 9–15 months Hiring + ramp | 6–12 months Deck before code | 6–14 weeks Vertical-slice pilot | 2–6 weeks. If the problem fits the template |
| Year-1 all-in cost | $650K–$1.4M (3 FTE loaded) | $450K–$2M+ | $30K–$340KDiagnostic + Pilot | $60K–$400K platform |
| Who writes the code | Your team, if hired right | Subcontractors, you never met them | Named senior engineers, public profiles | Auto-generated. You explain it to the auditors |
| Production-grade by default | Depends on the hire's discipline varies | Rarely Productionization out of scope | YesEval harness on day 7 | Platform-bound lock-in is the trade |
| Retraining + drift SLA | Your problemBuild it | Out of scope: Costs extra | In the SOW, a written commitment | Vendor-managed. If you stay on the platform |
| Vendor neutrality | Yours | Cloud-program-influenced | GenuineNo partner-program revenue | Locked by definition |
| Published pricing | Salary bands public | $2M+ rumorsOpaque | 4 tiers on this page | SaaS pricing tiers |
| Best fit | You will run 4+ DS engagements/year permanently | Multi-billion-scale transformation needing brand coverage | Strategy + production-grade build under one SOW, 2–18 month window | The use case is templated (forecasting, churn, fraud) and not your moat |
Our data science professional services begin with a 2-4 week diagnostic sprint to examine your business data, current systems, and overall goals. Once the problem is identified, a focused test is run to prove value with a single, specific feature or use case before expanding. A few clients jump straight to production hardening of someone else’s stalled models.
A complete, automated system that looks at past data to predict specific business events like demand, churn, LTV (lifetime value), propensity, and time-series forecasting. We ship the model, eval harness, drift monitor retraining schedule, not a notebook.
We use data to figure out which of your ads, emails, and social posts are actually making your money by segmentation, attribution, recommendation, and lifetime-value modeling. We connect it to your CRM and ad platforms, with ROI tracked at the campaign level.
It includes SKU-level forecasts, inventory optimization, network design, and supplier risk modeling. This approach is 2.7 times more accurate for everyday consumer goods.
We use advanced AI that reads and understands text just like a human. This allows our system to understand documents, perform smart search, perform sentiment analysis, summarize, use RAG architectures, fine-tune LLMs, and support audit logging.
Use smart cameras and video to help machines see and manage daily work. It involves quality inspection, defect detection, asset monitoring, and video analytics. Edge deployment where latency matters; cloud where it doesn’t.
A recommender system suggests things people might like based on their past behavior. They use smart math and real-time scoring to show the relevant content. By using A/B testing from day one, developers can instantly prove which changes actually improve business goals.
Getting your AI model to work in a Jupyter notebook is just the first step. We create automated safety checks whenever you change your code or update the model, including monitoring, drift detection, retraining pipelines, & audit logging.
Data engineering lays the technical plumbing required to feed the machine learning and analytics. Modern data architecture includes pipelines, lakehouses, dbt models, and feature stores.
We create easy-to-use business reports and tracking screens that show exactly what is happening in your company. We can build these reports using whatever software your business prefers, like Microsoft Power BI, Tableau, Looker, or Hex.
Your previous vendor shipped a notebook, but you can’t deploy it. We diagnose the production blockers (no eval, no monitoring, data leakage, drift, cost runaway) and harden them. ~30% of our 2026 engagements start this way.
We’ve already provided our data science service offerings multiple times across these industries. We’ve shipped multiple times to production with an eval harness that withstands real-world workloads.
Fraud detection, credit risk, AML, KYC, customer lifetime value. SR 11-7 ready. Benchmark: 38% fraud reduction in claims.
Clinical predictions, claims fraud, drug discovery, and patient adherence. HIPAA + BAA. Audit-ready pipelines.
Demand forecasting, pricing, assortment intelligence, and recommendation. Benchmark: 2.7× forecast accuracy uplift, $30M inventory savings.
Predictive maintenance, OEE improvement, quality control, throughput optimization. Benchmark: 2.5% OEE uplift across 40+ plants.
Route optimization, ETA prediction, fleet utilization, and dynamic dispatching. Cuts last-mile cost by 12–18%.
Multi-touch attribution, segmentation, LTV, ad-mix modeling. Benchmark: +25% ROAS, 10% media-spend savings.
Churn prediction, expansion playbooks, product analytics, feature usage modeling. Benchmark: 22% churn cut on a B2B scale-up.
Risk-based pricing, claims fraud, underwriting automation, and customer retention. Audit binders + explainability.
Cross-link — specific deep dives on machine learning development, generative AI consulting, and custom LLM development.
Score My ReadinessMost data science projects fail because the data is not ready, not because the model is wrong. Answer the following questions, and we’ll score you out of 100. We answer this honestly, even when it disqualifies you from buying. You’ll get the output after the contact step.
Every phase offers you a written go/no-go decision, and you can walk on either side. We have killed two of our own engagements at the diagnostic stage in the last 18 months, both at our recommendation.
This phase involves a 30-minute scoping call with a principal. If it feels like a mutual fit, then it’ll be paid. Output: written go/no-go memo with recommended tier.
A single fully functional feature is created and released in the basic version within 4 weeks. Focus on solving just one specific problem for real people using real information.
Use the 7-axis scorecard to determine whether the model is ready for use. It includes accuracy, drift, latency, cost-per-inference, governance, data freshness, and ROI released. Pass=proceed, fail = stop.
The system is made strong and reliable by following MLOps practices, including monitoring, audit logging, cost telemetry, and drift detection. Eval harness runs on every code change.
Getting users to adopt new tools requires effective training, easy-to-use dashboards, and a smooth integration into their workflow. The reason why 78% of our pilots reach production is that we focus on these human factors.
Continuous maintenance that makes sure that the Statement of Work (SOW) is tightly scoped and actionable. This phase involves automated baseline tracking, written drift SLAs, and runbooks. Optional MLOps retainer or hire-to-perm.
The RAND 2025 report on enterprise AI projects, the MIT NANDA State of AI 2025 findings on generative AI, and our own diagnostic interviews with 200+ buyers all point to the same ten patterns. Each one is preventable. None of them is about model selection.
A notebook is shipped with the “LGTM” visual used during demos, which masks structural failures. When real workloads arrive, input-output patterns change, causing performance to degrade silently, and there is no way to measure where it occurs.
A data scientist relies on clean, organized data to build models, but raw data is usually messy. Data engineering, ETL, instrumentation, and a warehouse must be in place before DS can do anything.
Model drift usually happens when real-world changes, such as evolving customer behavior, new economic conditions, or new trends, occur. It happens because the vendor leaves after delivering the model, nobody monitors it, it degrades silently, and six months later, nobody knows it.
Hiring the wrong first data role, such as a PhD modeler when you need a data engineer, usually happens because companies confuse insights with infrastructure. This mismatch delays product timelines and burns the runway.
The team disappears for 3 months, building infrastructure without adding any visible features. Because stakeholders couldn’t see the progress, they lost patience and canceled the project before the model was even used.
The data team gravitates toward the deep learning demo, not the boring forecast. Ignoring a high-yield, boring forecasting project leaves millions on the table. Building an exciting thing that nobody actually needs burns through money without making any.
There’s no true owner; an innovative team funds a pilot. If that sponsor leaves or is reassigned during a corporate reorganization, the project loses its voice, funding, and path to actual production, leaving it abandoned in the POC (proof-of-concept) graveyard.
A beautiful, unused dashboard often fails because it solves the builders' needs instead of the users'. It is built on untrustworthy data or requires excessive effort to access. As a result, the project becomes an orphan and a PoC graveyard.
During the testing phase or POC, the company tests a small number of inferences. Paying $1.20 each seemed perfectly fine; however, at the production scale, a volume of 50M/month is a big issue, and the project was pulled.
The vendor took the brief verbatim, built what was asked for, and delivered what nobody actually needed. Took the money and damaged the buyer's career internally.
Already in a PoC graveyard? ~30% of our 2026 engagements are rescues of someone else’s stalled model. The first conversation is free.
The Data Eval Gate is the methodology that lets us publish a 78% PoC-to-production rate. Every data science service engagement we ship has a measurable score on each of these seven dimensions before it’s allowed to graduate from pilot to production. Same framework we use for custom LLM development, AI chatbots, and ML programs. If you’re looking for a custom AI development company, we’re the one. Here’s why:
500–5,000 labeled examples representative of your real workload. Score logged on every code change. Regression-blocked in CI.
Data drift and concept drift are continuously monitored. Threshold breaches trigger alerts. Drift SLO is contractual, not aspirational.
Worst-case 95th percentile. For real-time scoring <200ms. For a batch <15 minutes. SLO bounded during the pilot, not after.
Dollar-cost-per-million predictions for the chosen tier. Re-evaluate quarterly cloud price changes; your architecture should benefit.
How stale can features be before predictions degrade? Determines whether you need streaming, hourly, or daily refresh.
Feature lineage, model cards, fairness audits, SR 11-7 binders. For regulated industries, this is the binary go/no-go.
Dollar value of being right vs wrong, attributed in code at the API gateway. The single number a CFO can sign off on.
Most data science services companies put “custom quote” on their pricing page because their margins depend on you not knowing what comparable work costs. Here’s what every engagement we’ve sold in the last 18 months actually fell into. Ranges are real, and we’ll pin a number in the SOW within 5 business days of the discovery call.
Want a specific number for your scope of data science consulting services? The TCO Calculator does it from your own inputs.
Open the CalculatorWalk through five questions about your data, volume, current baseline, and risk profile. We’ll show you the realistic year-1 engagement spend and a defensible savings figure with the math written out for your CFO. Output appears after the contact step.
The single most-asked question on Quora about data science consulting services. Buyers default to a fixed fee; practitioners universally warn that it’s the worst model when the scope is undefined. Here’s how we map the four engagement models to your reality.
| Dimension | Hourly / T&M | Fixed-fee project | Retainer | Embedded squad |
|---|---|---|---|---|
| When scope is… | Undefined or exploratory | Clearly bounded, signed off | Ongoing advisory or maintenance | Multi-use-case program |
| Cost predictability | Low | High (until change orders) | Medium (capacity-based) | Medium-high |
| Risk holder | You | Vendor (in theory) | Shared | Shared |
| Best for | Diagnostic Sprint | Vertical-Slice Pilot & Production Build | MLOps + drift monitoring & CTO advisory | Multi-use-case enterprise program |
| Typical rate/band | $150–$300/hr | $30K–$240K project | 8–16 hr/mo at principal rate | $180K–$900K / year |
| Watch out for | Scope creep & cost runaway | Change orders vendor undercuts on scope | Capacity is unused if the quarter is slow | Becomes captive team / fails hire-to-perm |
Most data science development firms profit from the build-to-suit verdict, so they recommend it. We pre-commit to the build-or-buy verdict before scope discussions. Here’s how it tends to play out, based on the 200+ engagements we’ve diagnosed since 2023, including off-the-shelf platforms named directly.
| Use case | Default verdict | When to flip it | Year-1 spend (buy) | Year-1 spend (build) |
|---|---|---|---|---|
| Sales/churn forecasting | Buy | If the churn definition is proprietary or industry-specific | $30K–$90K DataRobot | $60K–$140K |
|
Demand forecasting
|
Hybrid | SKU breadth + promotional complexity drive build | $45K–$120K H2O / DataRobot | $84K–$200K |
|
Fraud detection
|
Hybrid | Off-the-shelf often misses domain-specific patterns | $60K–$180K Feedzai / Sift | $120K–$240K |
|
Recommendation engine
|
Hybrid | If recommendations are core to product experience, build | $30K–$100K Algolia / Recombee | $72K–$170K |
|
Customer support classification
|
Buy | If volume justifies tuning | $24K–$60K SaaS | $54K–$120K |
| Computer vision/quality inspection | Build | Defect classes are usually plant-specific | N/A reliably | $96K–$240K |
|
Risk scoring / SR 11-7
|
Build | Regulator wants an auditable, owned, explainable model | N/A (regulatory) | $140K–$340K |
|
Product-embedded data feature
|
Build | If it’s what customers pay for, you cannot buy it | N/A | $200K–$900K+ |
HIPAA, GDPR, SOC 2, SR 11-7, EU AI Act, and NIST AI RMF are six frameworks and regulations that guarantee data privacy and mitigate financial risks. Knowing which to apply saves time and reduces procurement delays. Most US data leaders only learn what applies when a procurement reviewer flags it. Answer three questions to see your obligations. And the best part? This one is free.
Pick the closest match. Each tier carries a different framework.
Your combination of data sensitivity, geography, and decision profile triggers multiple frameworks. The list below is the obligations checklist for your engagement.
We don’t believe in a sales sheet listing preferred stacks. We have expertise across every layer of data science and analytics services, and a written Eval Gate decision for each engagement. Here’s what was in rotation across our last 40 production deliveries.
Most of the data science development firms pitch another team, whereas in reality, a different team is working on their project. But we’re different; we do what we commit, guaranteeing that the same team you meet in week 0 will review your model in week 6. We do this because we own the outcome of our data science and analytics services.
Leads a team that builds data-driven models and statistical analysis for the healthcare and insurance industry. Responsible for writing the first version of the Trango data eval gate. Earlier, I was a senior ML platform engineer at a top 3 EHR vendor.
She scales machine learning systems so they actually run reliably and cost-effectively in production. Managed a high-growth SaaS ML platform, scaling monthly inference from $40k to $4M. Knows where the dollar leaks.
Followed heavy-duty compliance rules for finance SR 11-7, artificial intelligence ISO 42001, and the EU AI Act. Successfully built governance frameworks for two of the world’s largest financial companies (Fortune 500).
An analytics engineer with a track record of building decision systems that drive actual business outcomes. Formerly the head of analytics engineering at a B2B SaaS unicorn. I specialize in turning complex data from dbt, Looker, and Tableau into actionable insights. Designed for people, not just for the show.
He runs ROI models to determine whether a new AI project is actually worth the investment. With a decade of experience at Bain & Company. He is the principal who pushes back if the use cases don’t pay back.
Design a data-driven system to catch fraud, prevent money laundering (AML), and accurately calculate credit risk. Regulatory proofing under SR 11-7/SR 26-2 ensures that AI models meet strict regulatory standards.
Most data science development firms ignore these legal protections and regulations. But Trango Tech makes sure to clarify them so you don’t have to ask. Here’s an overview of our standard statement of work (SOW); you can find the full version in the deck.
“All code, prompts, eval sets, fine-tuned weights, and architecture artifacts produced under this engagement vest in Client on payment. Trango retains no license-back.”
“Client data and any derived embeddings, features, or model artifacts are deleted on engagement close to written certification within 14 days.”
“As a trusted data science services company, we protect your data. Trango will not use Client data to train models for any other client, internal R&D, or portfolio assets without separate written consent.”
“Trango monitors deployed model drift for 90 days post-cutover; if drift exceeds the agreed threshold, retraining is performed at no additional cost.”
Have a look at a few of our successful custom data science solutions. Apart from these, we’ve also delivered eleven more services that we can walk you through. You can see them after signing an NDA Non-disclosure agreement. Reach out to us for the deck, covering examples across seven different business industries.
Healthcare · Fraud
An artificial intelligence system was built for an insurance company to automatically detect and flag fraudulent claims. It was tested for over 3 months and took 4.5 months to become fully operational. It uses gradient boosting, is trained on 240 million past claims, and is drift-monitored. It shipped with the SR 11-7 binder. Held at 0.6% false-positive rate against a 5,000-case gold standard.
CPG · Demand fc
A system was built to predict exactly how many of 2400 SKUs will be bought in the future. The team built and launched the entire system in just 14 weeks. It used a hybrid statistical and machine-learning approach with promotional-effect modeling. It replaced a stalled Big-4 engagement that had stalled for 11 months.
B2B SaaS · Churn
A successful 8-week pilot to production and the resulting 92% customer satisfaction. It is connected to Salesforce and product telemetry. The customer success playbook automatically generates “at-risk” accounts. It resulted in 92% adoption of the customer success team within 60 days.
More case studies under NDA, ask for the deck.
Most data science development firms never disclose their pilot-to-production ratio. But we tell you ours, then refresh it every quarter. Here, we’ve listed four numbers for the running average across all 2024-2026 engagements.
Sigmoid sent us slides, and Big 4 sent subcontractors we’d never met. Partnering with Trango Tech for data science professional services was the best decision. They walked in and told us that we needed data engineering before any model. They then shipped fraud detection to production in 18 weeks. They followed the same principles from week 0 to cutover.
Use the following points as a checklist when evaluating firms for data science services.
We provide one statement of work (SOW), one profit & loss statement (P&L), and one set of principals. The same team will provide the data strategy and data science services.
We have published our clear pricing across four tiers for custom data science solutions on this page. However, the custom quote will be provided after the discovery call, not before.
We publish the numbers and update them quarterly, and the audit binder is in every proposal. Industry average is 12-30% (RAND,2025).
We make sure that the model passes through every axis, including accuracy, drift, latency, cost, freshness, governance, and ROI. No model graduates from pilot to production until every axis passes.
We monitor your data science model’s performance for 90 days after it goes live. Watch it for drift, and if the drift gets too bad, we will retrain the model for free.
We’ve mentioned six principals for data science services on this page, and all operate directly out of our headquarters in Houston, TX. We do not outsource your project. All work is handled by our local team.
We don’t believe in upselling our services if we’re not the best fit for your solution. We even turned down 15% of inbound at the discovery stage because we didn’t find the right answer. Being honest is what makes us different from other data science firms. Here is the clear map. If your situation falls in the right column, we’ll not push you; instead, we will provide a strong referral.
Still have questions? The fastest path is a 30-minute principal call — not a BDR queue.
Data leaders confuse data science with analytics, ML, AI, and BI. Each discipline is different, with its own first hires and tools.
Tell us which data science solutions you are looking for. We’ll tell you in 30 minutes whether we’re the right data science development firm for you. If we’re not, we’ll refer you to the one. If we’re, you leave the call with a tier recommendation and a date for the next conversation.