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- Tasks: Join a team of Data Scientists to build AI data science solutions and models.
- Company: causaLens is revolutionising AI data science, empowering teams to create custom data science agents.
- Benefits: Enjoy 25 days paid holiday, share options, cycle to work scheme, and a learning budget.
- Why this job: Be part of a pioneering platform that enhances decision-making for top enterprises like Cisco and Johnson & Johnson.
- Qualifications: 2+ years in data science with Python; strong academic background in a quantitative field required.
- Other info: Collaborate with industry leaders and enjoy a vibrant team culture with happy hours and outings.
The predicted salary is between 42000 - 84000 £ per year.
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Direct message the job poster from causaLens.
causaLens is pioneering the world’s first platform for building AI data scientists – empowering everyone to create and deploy their own data science agents in days. Our platform enables teams to collaborate in a multi-agent environment, ensuring human oversight across the entire workflow and making AI-powered data science trustworthy and accessible to everyone – from analysts to business leaders.
We power industry leaders, including Cisco, Johnson & Johnson, Canon, and McCann Worldgroup, to accelerate and scale their data science capability. Join us to build the World’s First Platform for AI Data Scientists.
What we are looking for
We are looking for a Senior Data Scientist based in London to join our mission to create AI Data Scientists to radically advance decision-making for leading enterprises. You will join a team of 9 Data Scientists. We hire the top 1% of Data Science talent to create an intellectually stimulating environment where you can thrive and learn. You will be helping leading enterprises build their custom data science agents and help our users get the maximum out of the platform.
What you will do
As a Senior Data Scientist at causaLens, you will play a pivotal role in advancing our decision-making technology. This position demands a strong foundation in data science, particularly but not limited to time series, and using Python as the primary programming language. Some of your responsibilities will include:
- Using our Agentic AI framework to build data science solutions and models, using our platform on client-supplied data sets and use cases.
- Collaborating directly with business stakeholders to integrate domain knowledge into the modelling process, demonstrating how insights can enhance decision workflows.
- Crafting long-term visions and plans, in collaboration with clients and causaLens stakeholders, to successfully deploy agentic workflows into customers\’ strategies.
- Work closely with the product and engineering teams to shape the development of our platform.
- Communicate technical topics to non-technical audiences.
Requirements
- At least 2 years of commercial data science experience using Python.
- Please note that this and the following bullet imply a significant breadth and depth of technical skills we will be testing for during the interview process – e.g., Statistics; other programming/scripting languages; solid understanding and experience with Cloud technologies; OOP, TDD, GitHub/Actions/Flow, and MLOps best practices; classical ML algorithms; at least some NLP, etc.
- Strong academic record in a quantitative field (MEng, MSci, EngD or PhD).
- Excellent and proven communication and teamwork skills.
- Previous experience in high-growth technology companies or technical consultancy is a plus.
- Previous experience in sales, pre-sales, and/or other technical evangelism is a plus.
- Experience with consulting and/or customer-facing roles, especially in the supply chain, demand forecasting, retail/cpg, manufacturing, marketing, financial services, or the public sector is a plus.
- Experience with LLM and RAG, GenAI, and agentic workflows is a plus.
We care about our people’s lives, both inside and outside of causaLens. Beyond the core benefits like competitive remuneration, and a good work-life balance, we offer the following:
- 25 days of paid holiday, plus bank holidays.
- Buy/sell holiday options (up to 5 days).
- Share options.
- Happy hours and team outings.
- Cycle to work scheme.
- Friendly tech purchases.
- Benefits to choose from include Health/Dental Insurance.
- Special Discounts.
- Learning and development budget.
- 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.
If you require accommodations during the application process or in your role at causaLens, please contact us at
Seniority level
- Mid-Senior level
Employment type
- Full-time
Job function
- Consulting, Engineering, and Other
Industries
- Software Development
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Contact Detail:
causaLens Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied Data Scientist
✨Tip Number 1
Familiarise yourself with causaLens and their Agentic AI framework. Understanding their platform and how it empowers data scientists will help you articulate your fit for the role during discussions.
✨Tip Number 2
Network with current employees or alumni from causaLens on platforms like LinkedIn. Engaging in conversations about their experiences can provide valuable insights and potentially give you a referral.
✨Tip Number 3
Brush up on your Python skills, especially in relation to time series analysis and machine learning algorithms. Being able to discuss specific projects or challenges you've tackled using these skills will set you apart.
✨Tip Number 4
Prepare to demonstrate your communication skills by thinking of examples where you've explained complex technical concepts to non-technical stakeholders. This is crucial for the role and will show your ability to bridge the gap between tech and business.
We think you need these skills to ace Applied Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data science, particularly with Python and time series analysis. Emphasise any previous roles in high-growth tech companies or consultancy, as well as your academic achievements in quantitative fields.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for causaLens and their mission to create AI Data Scientists. Mention specific projects or experiences that demonstrate your ability to collaborate with business stakeholders and integrate domain knowledge into modelling processes.
Showcase Technical Skills: Clearly outline your technical skills in your application. Include your experience with cloud technologies, MLOps best practices, and any familiarity with LLM, RAG, or GenAI. This will help you stand out as a candidate who meets the technical demands of the role.
Prepare for Interviews: Anticipate questions related to your technical expertise and communication skills. Be ready to discuss how you've previously communicated complex technical topics to non-technical audiences, as this is crucial for the role at causaLens.
How to prepare for a job interview at causaLens
✨Showcase Your Technical Skills
Make sure to highlight your experience with Python and any other programming languages you know. Be prepared to discuss specific projects where you've applied your data science skills, especially in time series analysis and machine learning.
✨Communicate Clearly
Since you'll need to explain complex technical concepts to non-technical stakeholders, practice simplifying your explanations. Use examples from your past experiences to demonstrate how your insights have positively impacted decision-making.
✨Understand the Company’s Mission
Familiarise yourself with causaLens and its platform. Understand their approach to AI data science and be ready to discuss how your background aligns with their mission of making data science accessible and trustworthy.
✨Prepare for Collaborative Scenarios
Expect questions about teamwork and collaboration. Think of examples where you've worked closely with business stakeholders or cross-functional teams to deliver data-driven solutions, as this is crucial for the role.
