Workwave

Senior Applied Data Scientist / ML Engineer

United Kingdom, Remote

Actively hiring, first seen by us 5 days ago

Pay: Pay not listed
Work setting: Remote — work setting source
LOCATION & RIGHT TO WORK Location: Remote (UK-based) with flexible working options. Right to Work: Applicants must currently have the legal right to work in the UK. We are unable to offer visa sponsorship now or in the future for this position.
Listed location: United Kingdom, Remote
Read the full posting
Employment: Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting

Before you apply

Work authorization: Must already have the right to workLOCATION & RIGHT TO WORK

What you’ll work on

Full posting

Build missing features and data pipelines in Snowflake and dbt
Deploy, test, version and monitor models, including drift detection
Design A/B tests or causal analyses to prove features improved customer outcomes
Decide how predictions reach customers and report realised impact to the business

From the employer’s posting

create them in Snowflake and dbt rather than waiting for someone else to

Deployment, testing, versioning, monitoring, and drift detection.

design the A/B tests or causal analyses that prove a feature improved customer outcomes

Decide how a prediction reaches the customer so they understand it, trust it, and act on it

About Workwave

The Owner: You measure your work by whether customers made better decisions, not by whether the model shipped.

In the employer’s words · Read in context

Apply at Workwave

Posting record

Report an error

Actively hiringFirst seen by us 5 days ago, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Oct 30, 20265 days ago
Recorded sightings: 6
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 5

Early. Similar jobs usually come down between day 6 and day 69.

From 4,284 similar postings that came down. Coming down is not always a hire.

Workwave is hiring

1 open job

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
SQL
AWS
dbt
Snowflake
LightGBM
scikit-learn
XGBoost

Where the posting names them

- Experience: 5+ years in applied data science, ML engineering, or data engineering that included owning models in production—including at least one model you built and shipped into a real product. B2B SaaS experience is a strong plus. - Technical Core: Strong Python and applied ML libraries for tabular problems (scikit-learn, XGBoost or LightGBM, statsmodels or Prophet). Solid SQL expertise is required. - ML & Modeling Depth: Depth in supervised learning, forecasting, ranking, recommendation, or optimisation. You have modelled messy operational data, not benchmark datasets.

- ML & Modeling Depth: Depth in supervised learning, forecasting, ranking, recommendation, or optimisation. You have modelled messy operational data, not benchmark datasets. - Data & Delivery: You build the data you need and ship what you build—dbt and Snowflake modelling, feature pipelines, deployment, monitoring, and drift detection. We're on AWS. - Measurement & Narrative: You've quantified the business impact of a model you shipped—adoption, outcome, ROI—and presented it to people who were never going to read your notebook.

- Learn the Domain: Get fluent in the semantics of how our customers operate—what a route, a crew, or a service history actually means. A model that is accurate but wrong about the domain creates nothing. - Build the Data You Need: When the features don't exist, create them in Snowflake and dbt rather than waiting for someone else to. Prove It and Make It Felt

Upload resume Add LinkedIn

Your next step

Complete your application on jobs.lever.co. The employer’s form will show what is required.

Applying through Lever

No account needed: one form, and Lever reads your resume into the profile recruiters search.

Already applied? Track this application

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025: $140,300
Expected change, 2025 to 2035: 21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

We are looking for a product-minded applied data scientist or engineer to turn raw operational data into products that measurably improve how our customers make decisions and run their businesses. Which label you carry matters less to us than whether customers end up better off.

This is not a research-only role, nor a service-oriented internal analytics position—and it is not a role for a model builder alone. We want an owner: someone who frames the problem, learns the domain, builds the data when it doesn't exist, ships the model, and stays with it until customers are acting on it and can measure the reward. The road runs through data engineering; deployment and testing are part of delivery, not a handoff. You understand that great models are not just accurate in notebooks—they are usable, explainable, and measurable inside a real product.

Whether you came to this work through statistics, software, analytics, or data engineering, you have a strong bias toward shipping.

What You'll Do

From Raw Data to Better Decisions

Own the Outcome: Take an ambiguous customer problem, decide whether ML is even the right answer, build it, and stay with it until customers are acting on it.
Learn the Domain: Get fluent in the semantics of how our customers operate—what a route, a crew, or a service history actually means. A model that is accurate but wrong about the domain creates nothing.
Build the Data You Need: When the features don't exist, create them in Snowflake and dbt rather than waiting for someone else to.

Prove It and Make It Felt

Make the Value Legible: Decide how a prediction reaches the customer so they understand it, trust it, and act on it—then report realised impact back to Product and the business in numbers that hold up.
Measure Honestly: Define offline and online evaluation for model quality, drift, and reliability, and design the A/B tests or causal analyses that prove a feature improved customer outcomes.

Deliver

Ship and Operate: Deployment, testing, versioning, monitoring, and drift detection. Delivery is part of the job, not a handoff.
Embed with Product: Partner with Product Managers and Software Engineers to put ML inside real product workflows—and say clearly when ML isn't the answer.

Who We Are

The Owner: You measure your work by whether customers made better decisions, not by whether the model shipped.
Closer to the Data and the Customer: You'd rather spend a week understanding what the data means than a week tuning a model. You know the domain is the hard part.
A Multi-Disciplinary Operator: You'll chase down the data yourself when it isn't ready, and build the pipeline if that's what delivery requires. You prioritise usability, "Time to Insight," and customer trust as much as you do code efficiency.

We know that great talent comes from many backgrounds. If you have shipped a model you are proud of, we want to hear from you!

HOW WE WORK

We build with coding agents. You set direction and targets, review output critically, and build the harnesses—scaffolding, context, tests, review loops—that make the next model faster to ship. The leverage is in the verification: the backtests, eval scaffolding, and data checks that make generated work safe to trust.

What You'll Bring

Experience: 5+ years in applied data science, ML engineering, or data engineering that included owning models in production—including at least one model you built and shipped into a real product. B2B SaaS experience is a strong plus.
Technical Core: Strong Python and applied ML libraries for tabular problems (scikit-learn, XGBoost or LightGBM, statsmodels or Prophet). Solid SQL expertise is required.
ML & Modeling Depth: Depth in supervised learning, forecasting, ranking, recommendation, or optimisation. You have modelled messy operational data, not benchmark datasets.
Data & Delivery: You build the data you need and ship what you build—dbt and Snowflake modelling, feature pipelines, deployment, monitoring, and drift detection. We're on AWS.
Measurement & Narrative: You've quantified the business impact of a model you shipped—adoption, outcome, ROI—and presented it to people who were never going to read your notebook.
Communication & Collaboration: Excellent communication skills with the ability to explain complex technical trade-offs clearly to product, engineering, and non-technical business stakeholders.

Bonus Points For:

Working With Agents: You've used coding agents on real modelling or engineering work, you can tell correct output from merely plausible output, and you invest in the scaffolding that makes the next model faster to ship.
Decision Intelligence: Experience with decision intelligence, forecasting, customer behaviour modelling, workforce/route optimisation, or operational intelligence products.
Leadership: Prior experience as a senior or lead scientist or engineer responsible for guiding technical direction.
LLM / Agentic AI: LLM or agentic workflows shipped into a product.

LOCATION & RIGHT TO WORK

Location: Remote (UK-based) with flexible working options.
Right to Work: Applicants must currently have the legal right to work in the UK. We are unable to offer visa sponsorship now or in the future for this position.

WHAT YOU SHOULD KNOW ABOUT US

Laid back but focused: We offer a flexible, collaborative work environment and a passion for developing innovative, best-in-class solutions that directly contribute to the success of our customers.
Impact-driven: We care deeply and deliver services and solutions that make a real difference in the lives of our clients and their businesses.
Trust & Inclusion: We openly accept others as they are and build strong partnerships based on trust and mutual respect.

RELAX, WE'VE GOT YOU COVERED

Competitive company pension scheme with employer match.
Generous holiday allowance / flexible time off plus UK bank holidays.
Enhanced maternity, paternity, and parental leave policies.
Ongoing learning resources.
Comprehensive Employee Assistance Programme (EAP) providing free 24/7 mental health, legal, and financial guidance.

EQUAL OPPORTUNITY

WorkWave is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees in accordance with the Equality Act 2010.

Don't meet every single requirement? Studies show that women and underrepresented groups are less likely to apply for jobs unless they meet every qualification. If you feel you could make a great impact in this role, we encourage you to apply anyway!

Employment type

Full-time

Source details