Senior Forward Deployed Data Scientist Data Science · London · Hybrid Remote

Monolithai
London
2 days ago
Applications closed

Related Jobs

View all jobs

Senior Salesforce Business Analyst

Senior Quantitative Analyst (Energy Trading)

Senior Data Engineer

Senior) Research Manager Remote/Hybrid

Senior Customer Operational Services Coordinator

Senior Data Engineer

Are you passionate about revolutionising engineering with AI? Here at Monolith AI we’re on a mission to empower engineers to use AI to solve even their most intractable physics problems. We’ve doubled in size over the last four years, and we have ambitious plans moving forward. It’s an exciting time, and to continue our growth, we are recruiting a Senior Forward Deployed Data Scientist for our Data Science Team.

What you can expect as a Senior Forward Deployed Data Scientist at Monolith AI:

We are seeking a Senior Forward Deployed Data Scientist to implement our ML SaaS platform with major automotive and EV manufacturers. You’ll develop custom Python-based ML solutions for use cases like anomaly detection and time series analysis, bridging implementation gaps. Collaborating with Customer Success, you’ll tackle challenges in ML generalization, guide clients on model performance, and influence product improvements. While focused on delivering client projects, you’ll also support pre-sales discovery and mentor junior data scientists, shaping technical delivery strategies.

Key Responsibilities:

Technical Leadership

  1. Lead complex DS-heavy customer projects from inception to production deployment
  2. Define and refine technical success criteria for customer implementations
  3. Architect and implement custom ML solutions for enterprise clients
  4. Develop and optimise existing algorithms for specific customer use cases
  5. Write production-quality Python code for custom ML implementations
  6. Assess bespoke solutions for generalisation potential and platform evolution

Customer Engagement

  1. Drive hands-on technical support during custom implementation phases
  2. Present technical solutions and ML results to diverse stakeholders
  3. Serve as primary technical contact for assigned customer teams
  4. Troubleshoot technical issues related to model performance and implementation
  5. Partner with CS team members to provide technical expertise in customer reviews

Pre-Sales Support

  1. Evaluate technical feasibility of non-standard solution requests
  2. Assess implementation risks and provide realistic effort estimations
  3. Contribute to proposal documentation and statements of work
  4. Support proof-of-concept implementations for pre-sales validation
  5. Articulate technical differentiation against market alternatives

What would set you up for success coming into this role:

  1. You are highly autonomous in scoping and delivering end-to-end ML projects, from initial technical assessment through to results presentation and product feedback
  2. You are an excellent communicator who can articulate complex ML concepts to diverse audiences, adapting technical depth based on audience expertise
  3. You possess strong prioritisation skills with ability to manage multiple concurrent projects and proactively communicate timeline risks
  4. You have a strategic mindset to identify opportunities where custom solutions can be generalised into reusable platform components
  5. You are an expert at analysing trade-offs and making pragmatic technical decisions that balance solution sophistication against time-to-value
  6. You have the ability to assess multiple technical approaches and select solutions that align with both immediate customer needs and long-term platform evolution

For this role, it’s crucial that you have:

  1. 6+ years experience in Machine Learning, Data Science or Forward Deployed roles, with 2+ years direct customer exposure
  2. Advanced Python programming skills, including ability to build ML algorithms from scratch using frameworks like numpy, scipy, pytorch, pandas, sklearn
  3. Competent SQL skills including CTEs, joins, and ability to learn advanced concepts as needed
  4. Experience working in startup or early-stage environments
  5. Track record of delivering end-to-end ML projects in production environments

It would be a real bonus, but not a requirement if:

  1. Experience with time series analysis, anomaly detection, or deep learning
  2. Apache Spark knowledge/experience
  3. PhD in relevant field, research publications, or contributions to open-source projects
  4. Prior experience in successful startups

Interview Process:

  1. You’ll have a 20-minute conversation with one of our internal recruiters to get you excited about the opportunity.
  2. You’ll then have the opportunity to meet with our Director of Machine Learning and find more about the opportunity whilst having a short problem-solving exercise.
  3. You’ll then be sent a take-home task and spend 45 minutes discussing it with a couple of our team members.
  4. Lastly, you’ll have a 45-minute chat with a couple of our team to understand more about the teams you’ve worked in previously and of course there’ll be plenty of time to ask questions.

Why Monolith?

Our culture is passionate, engaging and collaborative. We are genuine, we bring our true selves to work and celebrate those little quirks that make us different. We have a culture of learning, we encourage new ideas, out of the box thinkers and risk takers. We’re all human and sometimes we make mistakes, but we brush ourselves off and try again. Our culture encourages freedom, flexibility and creativity.

At Monolith our values are core to how we do business. They’re not just words on a wall, we live them every day. Our values are embedded in our internal processes so that we’re always reminded of what’s important to us and we continue to grow as individuals and as a company.

Our values are:

  1. Bring yourself to work
  2. Always be curious and open
  3. Think like an engineer
  4. Work smart, not hard
  5. Be in this together

Our benefits & perks for UK employees:

  1. 30 days paid annual leave + bank holidays
  2. Pension with NEST
  3. Vitality health insurance
  4. Wellness allowance through Heka
  5. A day off to volunteer per year
  6. Regular socials

A few things to note:

  1. Monolith is proud to be an equal opportunity employer and we value diversity and inclusion. We welcome people of different nationalities, backgrounds, experiences, abilities and perspectives.
  2. We don’t have an end date to apply for this role, but we will prioritise early applicants, so if you’re interested then please apply soon.
  3. Only applicants who have the right to work in the UK will be considered.
  4. We are not open to working with external recruitment agencies at this time.
  5. If you don’t quite match everything above but you feel you can succeed in this role then we encourage your application and look forward to hearing from you.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.