Applied Data Scientist / ML Engineer

WorkWave; United States; Remoto; USD 100.000 - 147.000; Tempo pieno

Vantaggi offerti da questo lavoro

Competitive pension scheme

Generous holiday allowance

Enhanced parental leave

Ongoing learning resources

Employee Assistance Programme (EAP)

Descrizione del lavoro

WorkWave seeks a product-minded applied data scientist or engineer to transform raw operational data into customer-facing products that drive decisions and business outcomes. You own the process from problem framing to deployment, ensuring models are usable, explainable, and measurable in a real product context.

You will build data pipelines, work with Snowflake/dbt on AWS, and collaborate with Product and Engineering to embed ML within workflows.

Competenze

• 5+ years in applied data science, ML engineering, or data engineering with production models.
• Strong Python and tabular ML libraries; solid SQL knowledge.
• Experience with dbt and Snowflake modelling, deployment, monitoring.
• Expertise in forecasting, ranking, recommendation, or optimisation.
• Leadership experience guiding technical direction on real products.
• Proven ability to quantify business impact of a shipped model.

Mansioni

• Own the outcome from ambiguous customer problems to measurable value.
• Learn the domain semantics of customer operations (routes, crews, service history).
• Build required data in Snowflake/dbt and feature pipelines.
• Define how predictions reach customers and report impact with numbers.
• Design offline/online evaluation, A/B tests, or causal analyses to prove value.
• Deliver, deploy, monitor, and manage drift in production models.
• Embed ML in product workflows with Product and Engineering teams.

Conoscenze

Python

SQL

ML engineering

Forecasting

Leadership

Communication

Agentic AI

Strumenti

Snowflake

dbt

XGBoost

LightGBM

scikit-learn

AWS

Prophet

Kubernetes

Descrizione del lavoro

Описание:

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.

Задачи:

• 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.

Требования:

• 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.
• 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.

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.

Условия:

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! 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.