Senior Engineer - Data Science Operations

causaLens is the pioneer of Causal AI — a giant leap in machine intelligence.

We are on a mission to build truly intelligent machines, machines that truly understand cause and effect— it’s hard but super fun! If you want to build the future and are looking for a place that values your curiosity and ambition, then causaLens is the right place for you. Everything we do is at the forefront of technological advancements, and we are always on the lookout for people to join us whose skills and passion tower above the rest.

Since the company was established in 2017, causaLens has:

🥳Launched decisionOS, the first and only enterprise decision making platform powered by Causal AI - here

🚀Open sourced two of our internal tools and packages to support the open-source community, see Dara and Causal Graphs.

🦄Raised $45 million in Series A funding

🏆Been named a leading provider of Causal AI solutions by Gartner - here

🚀Included in Otta’s 2022 Rocket List as one of the fastest-growing companies to launch your career

At causaLens we are building the world's most advanced Causal AI powered decision intelligence platform for Data Scientists. The platform leverages state of the art Causal AI algorithms and models to empower data scientists and decision-makers to go beyond correlation-based predictions and have a real impact on the most important decisions for the business. Our platform is trusted and used by data science teams in leading organizations and provides real value across a wide variety of industries, and it's only the beginning.

Our MissionTo radically advance human decision-making.A world in which humans leverage trustworthy AI to solve the greatest challenges in the economy, society and healthcare.Head to our website homepage and watch the ‘Why Causal AI’ video to learn more.

The Role

We are seeking a talented individual with a unique blend of Machine Learning Engineering and Data Science experience to join our team in a role focused on optimizing the path to production for Data Scientists. This role will primarily involve empowering Data Scientists to independently deploy their work into production.

A day in life:

• Collaborate closely with data scientists to understand their workflow challenges and requirements for deploying their work into production.

• Develop and enhance mechanisms and processes to streamline the integration of causal AI models into the MLOps workflow, customizing it to suit causality.

• Design and implement tools, visualizations, and mechanisms that empower data scientists to deploy their work into production autonomously, reducing dependencies on additional teams or assistance.

• Work directly on integrating key elements of MLOps workflow with causal AI capabilities, ensuring robustness, scalability, and efficiency.

• Act as a subject matter expert on processes and best practices, providing guidance and support to data scientists on deployment and monitoring.

• Collaborate with cross-functional teams including data science, software engineering, and product to align technical solutions with business objectives and user needs.

Requirements

This role offers a unique opportunity to leverage expertise in both machine learning engineering and data science to drive transformative changes in empowering data scientists with advanced capabilities in causal AI. If you are passionate about streamlining the path to production for data scientists and accelerating the adoption of causal AI technologies, we encourage you to apply and contribute to our team.

You have:

• Bachelor's or Master's degree in Computer Science, Physics, Maths, or a related field or equivalent industry experience.

• 3-5 years of professional experience in machine learning engineering, data science, or a related role, with significant exposure to deploying machine learning models into production.

• Strong proficiency in Python for both machine learning engineering and data science tasks.

• Experience with MLOps practices, including model deployment, monitoring, version control, and CI/CD pipelines.

• Proven experience in data pipelining, system architecture, and design for scalable and reliable production environments.

• Strong communication skills with the ability to convey technical concepts to both technical and non-technical stakeholders.

• Excellent attention to detail and commitment to delivering high-quality work in a fast-paced environment.

About causaLens
Current machine learning approaches have severe limitations when applied to real-world business problems and fail to unlock the true potential of AI for the enterprise. causaLens is pioneering Causal AI, a new category of intelligent machines that understand cause and effect — a major step towards true artificial intelligence. Our enterprise platform goes beyond predictions and provides causal insights and suggested actions that directly improve business outcomes for leading businesses in asset management, banking, insurance, logistics, retail, utilities, energy, telecommunications, and many others.

We may be biased, but we believe you’ll be in good company. We offer a hybrid working setup and are dedicated to building an inclusive culture where diverse people and perspectives are welcomed. Aside from joining a smart and inspiring team, you’ll be amongst people who are always there to support your ideas and encourage you to grow. We celebrate our differences and come together to share our triumphs!

What we offer
We care about our people’s lives, both inside and outside of causaLens. Beyond the core benefits like competitive remuneration, pension scheme, paid holiday, and a good work-life balance, we offer the following:

• Access to mental health support through Spill

• Competitive salary

• 25 days of paid holiday, plus bank holidays

• Share options

• Pension scheme

• Happy hours and team outings

• Referral bonus program

• Cycle to work scheme

• Friendly tech purchases

• Office snacks and drinks

Logistics

Our interview process consists of a few screening interviews and a "Day 0" which is spent with the team (in-office). We will always be as transparent as possible so please don’t hesitate to reach out if you have any questions.