Skip to content
Arixent

Data Science & Engineering

Data Science & Data Engineering Services

Reports disagree when pipelines, metric definitions and ownership grow separately. We connect data architecture, engineering, analytics and predictive models so teams can trace the information behind a decision and keep it current.

Talk to an engineer
Blue particle stream with glowing amber dots on black

In this section

Bring us the roadmap. We will bring the engineers.

Talk to an engineer

AI ambitions are only as good as the data underneath them.

Every stalled analytics program and every AI pilot that never left the lab share a root cause: data that is late, inconsistent or nobody trusts. Our data science and engineering services fix that in the right order. We modernize the platform, build reliable pipelines, put governance in place where it matters and only then layer on analytics and predictive models. The result is a data estate your teams use daily and your AI initiatives can depend on.

Data Strategy, Architecture & Governance: Target architecture, operating model and the governance that makes data trusted and AI-ready.

Data Engineering & Platform Modernization: Batch and streaming pipelines, lakehouses and migrations off legacy warehouses.

Analytics, BI & Customer Insights: Semantic layers, dashboards and customer analytics people actually use.

Data Science & Predictive Modeling: Forecasting, churn, risk and optimization models, delivered by embedded data science teams.

Blue particle stream with glowing amber dots on black

Need a delivery plan before you commit to a team?

Book a working session

Capabilities

What we deliver in Data Science & Engineering

Four offerings cover the data estate end to end. Each one lists the work inside it, so scoping starts from named capabilities.

Executive presenting charts to a leadership team in a dark boardroom

Data Strategy, Architecture & Governance

Target architecture, operating model and the governance that makes data trusted and AI-ready.

Explore the offering
Blue fiber optic strands rising on a dark background

Data Engineering & Platform Modernization

Batch and streaming pipelines, lakehouses and migrations off legacy warehouses.

Explore the offering
Analyst reviewing a business dashboard on a laptop

Analytics, BI & Customer Insights

Semantic layers, dashboards and customer analytics people actually use.

Explore the offering
Analyst pointing at forecast charts on a glass display

Data Science & Predictive Modeling

Forecasting, churn, risk and optimization models, delivered by embedded data science teams.

Explore the offering

How we work

How we approach data work

  1. 01

    Understand the decisions the data must support

    Start with the business decisions, users and measures the data estate needs to serve.

  2. 02

    Fix the foundations: platform, pipelines, quality

    Address architecture, movement and trust before adding more consumption layers.

  3. 03

    Deliver analytics and models people use

    Build outputs into the tools and routines where teams make decisions.

  4. 04

    Operate and govern as the estate grows

    Keep ownership, quality, access and cost visible as use expands.

Industries

Industries

Data work grounded in the systems, decisions and operating context of each industry.

Shopper checking her phone while picking fruit in a grocery store
Retail & E-commercePersonalization, demand forecasting, search and customer data platforms.Explore
Financial charts layered over a blue city skyline
Banking, Financial Services & InsuranceDocument intelligence, risk models, fraud detection and modern digital banking experiences.Explore
Container terminal with cranes and stacked containers at sunset
Logistics & Supply ChainForecasting, route and inventory optimization, visibility platforms and control towers.Explore
Row of white robotic arms on an automated production line
Manufacturing & IndustrialPredictive maintenance, quality inspection, digital twins and plant analytics.Explore
Solar panels and wind turbines against a sunset sky
Energy & UtilitiesAsset analytics, load forecasting, field operations and customer platforms.Explore
Wireless network links glowing over a city at night
Telecom & MediaNetwork analytics, churn prediction, content recommendation and OSS/BSS modernization.Explore

FAQ

Questions we hear often

Snowflake, Databricks, Google BigQuery, Amazon Redshift, Azure Synapse and Microsoft Fabric, along with lakehouse architectures on open formats such as Delta, Iceberg and Parquet.

Yes. We migrate incrementally, run old and new in parallel, reconcile outputs and switch consumers over domain by domain.

Both. Analytics and BI is a full service line, and our data engineers and analysts work together so dashboards sit on a governed semantic layer instead of ad hoc queries.

Quality checks are built into pipelines with alerts and ownership, and a governance layer tracks lineage, definitions and access. Quality is a system, not a cleanup project.

Also from Arixent

Related practices

AI Engineering

Generative AI, agents, machine learning and computer vision, taken from the first use-case workshop to systems running in production with evaluation, monitoring and governance in place.

Product Engineering

Discovery, design, development, cloud and quality engineering for SaaS and enterprise products, from the first MVP to the tenth major release.

Next step

Ready when you are.

Tell us what you are trying to build and we will come back with a point of view, not a pitch.

Start a conversation