Data Science function hiring in: United Kingdom

Engagement Option(s): Full Time

Work Preference Option(s): Hybrid | In Office | Work From Home

This is a perfect opportunity for ambitious individuals to join a team of curious minds and supporting peers with a passion and the skills for creating value for businesses using data.

About Us

We are Ascent! and we help our customers solve problems, elevate, and do existing things better. We are on a mission to help our customers connect data, software, and purpose to create extraordinary outcomes. You could say we are a digital transformation business.

We specialize in software product development, analytics, data science, IoT solutions, machine learning, DevOps optimization, and modernization of applications, data, and platforms.

We work with incredible clients in all types of industries such as smart home devices, space exploration, beer manufacturing, finance, ecology, and logistics. We work with some of the sharpest minds in the brightest businesses and we employ the sharpest minds too!

At Ascent, we also believe in fostering a vibrant office community where collaboration thrives and connections flourish. With our hybrid approach, we prioritize hiring individuals who reside in close proximity to our central offices in Bristol and London. Our aim is to cultivate a positive atmosphere and sense of belonging by facilitating easy access to the office. However, we welcome applicants from all other areas in the UK, as we value diversity and recognize the unique perspectives each individual brings to our team. Join us in shaping a workplace where proximity enhances collaboration while inclusivity strengthens our community.

About the role

A Senior Data Scientist (MLOps Engineer) leads the development of complex features of projects, demonstrating advanced problem-solving skills and overseeing the entire lifecycle from inception to development. They engage in cross-functional collaboration, proactively address technical challenges, and ensure solutions align with business requirements. They look at actively seek opportunities to maximise project scale-up. They have widely recognised strengths (specialities) that have directly impacted our ability to stay and expand on an account and have contributed to internal upskilling and knowledge sharing. Seniors are expected to be aware of commercial value of solution / project, being able link output (ML predictions or process automation) to financial impact and contribute to project case studies

Responsibilities

As a Senior Data Scientist, you will:

Become a trusted consultant for the customer and go beyond a “developer” or “engineer” mindset in all engagements.
Provide L3 support for managed service customers as relevant (e.g. MLOps)
Advise customer on technical solutions to business problems, actively identifying and gauging gaps in customer’s capabilities, capacity to inform account approach.
Implement data science solutions and support end-to-end pipelines.
Challenge solution design and methodology with innovative ideas.
Apply statistical rigour, software development discipline and computational efficiency in project delivery.
Plan and estimate for delivery, support project management from the technical perspective.
Advocate for DevOps skills, disciplines, principles.
Ownership of at least parts of deliverables, including definition of success, implementation, quality control and presentation of output
Main contributor to internal asset build (PoC, Assets)
Drives team towards technical excellence, considering technical debt, system design, stability, and business needs.
Bring impactful, high-quality technical contributions within the team.
Aid wider solutioning and opportunity discovery by learning (high level) Ascent capabilities and technologies, e.g. MSFT Fabric, Databricks, Terraform, React/Blazor frameworks, Service blueprint.
Adapt technical scope to align with changing business priorities.
Make pragmatic trade-offs between perfection and technical debt which aligns with the project needs, including how to repay debt.
Implement temporary workarounds when necessary to restore service, with plans for permanent resolution.
Prioritise fixes for immediate issues while considering the long-term impact on system stability.
Communicate effectively with stakeholders about the trade-offs involved in different solutions.
Work competently on complex and unknown problems under pressure (e.g. debugging complex production issues or leading the resolution of critical or major incidents)
Engage in architectural discussions and proposes innovative solutions.
Ensure code quality through rigorous testing and reviews.
Contribute to forums and initiatives to improve practices.
Design scalable systems and optimises code for performance.
Streamline development workflows through proposed solutions.
Drive adoption of new tools and technologies within the team.
Share knowledge and best practices to enhance team productivity.
Take ownership of code quality and actively seeks opportunities for improvement.
Refactor code for readability, maintainability, and performance improvements when appropriate.

Requirements

Expertise in designing scalable and efficient machine learning architectures, including the implementation of feature stores for managing and serving features to machine learning models.
Proficiency in distributed computing technologies for handling large-scale data processing, training machine learning models, and serving predictions at scale.
Deep understanding and practical experience with various Microsoft Azure services relevant to machine learning, including data storage, monitoring, and containerization (e.g., Entra, Storage Account, Azure Monitoring, Azure Container Registry).
Familiarity with Windows Subsystem for Linux (WSL), Docker containers, and Python wheel files for packaging and distributing machine learning models and their dependencies across different environments.
Ability to set up and configure Azure MLOps accelerator for new machine learning projects, ensuring standardized and efficient development, deployment, and monitoring processes.
Can contribute to the development of the Azure MLOps accelerator.

Qualifications

Certifications: Azure Data Scientist Associate DP-100, Azure Data Engineer Associate DP-203, Databricks Certified Machine Learning Professional (nice to have: AZ-400)
5+ years of data science experience
Right to work in UK and/or EU

Working at Ascent

At Ascent we promote a healthy work-life balance by offering flexibility in where you work. We also promote well-being and provide access to Well Being Coaches.

Your development and learning will be taken seriously, and we’ll support your professional development with training and certification, with regular feedback and review. It is a fun, supportive and modern workplace where we really live by our company values of Empathy, Energy and Audacity! Ascent also offers a variety of benefits in each of our countries.

Ascent is an equal opportunities employee. We take intentional steps to ensure inclusion and belonging are something real here, not just something we talk about. No person will be treated less favorably because of their gender, pregnancy, and maternity status, marital or civil partnership status, sexual orientation, race, nationality, ethnic origin, age, religion or belief, or disability status. If you require any reasonable accommodation, please let us know when you apply.

If you have any questions contact our Talent Acquisition team on ta.admin@ascent.io.

For more details about life at Ascent, check out our Life Page.

Ascent

New Jobs

Senior Data Scientist (with an MLOps Engineering focus)