Senior Data Scientist at BLACKLANE

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

09 Apr, 26

Salary

0.0

Posted On

09 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, AWS, Model Evaluation, Feature Engineering, Collaboration, Problem Solving, Statistical Analysis, CI/CD, Automation, Monitoring, Communication, Cloud Computing, Analytical Skills, Proactivity, Reliability, Scalability

Industry

Travel Arrangements

Description

YOUR CHALLENGE: As a Senior Data Scientist within Blacklane's central data domain, you will leverage scientific and mathematical methodologies to address complex data challenges prevalent in the mobility and luxury transportation sector. In this role, you will also play a key part in scaling the data science team by owning and evolving our machine learning platform and operating model. This role involves close collaboration with product, engineering, and other data teams to enhance profitability, optimize costs, and accelerate growth. You will also engage with diverse business stakeholders globally. Beyond individual use cases, you will be responsible for how machine learning solutions move from idea to production and remain reliable over time. This position provides avenues for continuous professional development, flexible work options, and participation in an inclusive and diverse work environment. Design, build, and maintain end-to-end machine learning systems, supporting use cases such as demand forecasting, pricing optimization, customer lifetime value, and churn prediction. Establish best practices for experimentation, reproducibility, and model evaluation, while analyzing complex datasets to uncover trends, patterns, and actionable insights. Collaborate with cross-functional teams to understand business needs and translate them into scalable scientific and machine learning solutions. Collaborate with stakeholders at various levels to propose, discuss, and drive scalable data science solutions, clearly communicating tradeoffs and impact. Design and evolve shared ML pipelines, tooling, and standards to enable the team to scale efficiently and safely ABOUT YOU: Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, or a related field. Minimum 5 years of experience in a data science or machine learning engineering role. Proven track record in designing and deploying machine learning models into production environments, especially using AWS services (e.g. SageMaker, EC2, S3, Lambda, Step Functions). Experience working with cloud-based ML platforms (e.g. AWS SageMaker or equivalent services). Demonstrated proficiency in modeling business problems through the application of machine learning techniques, including feature engineering, model selection, implementation, evaluation, and parameter tuning. Ability to translate business challenges into mathematical and statistical problems, subsequently proposing and implementing data science solutions. Strong judgment in selecting appropriate models and designing systems that balance accuracy, reliability, and scalability. Hands-on experience with modern data science tools and ML frameworks, combined with experience building or improving shared ML pipelines and tooling. Strong analytical and problem-solving capabilities, with the aptitude to interpret complex datasets. Strong experience operationalizing machine learning systems end to end, including CI/CD, automation, monitoring, and retraining. Exceptional communication skills, with a collaborative mindset and experience influencing technical direction across teams Self-driven, curious, and proactive in staying current with developments in ML, MLOps, and cloud computing. About Blacklane Our mission is simple but powerful: to become the world’s leading premium, global chauffeur service. Operating in 50+ countries, we reimagine chauffeur-driven rides by prioritizing reliability, innovation, and first-class service at every step. Blacklane is also at the forefront of sustainable mobility efforts, continuously working to reduce our operational impact on the planet. Our Company Culture Blacklane has grown from a two-person startup in Berlin to a global team of over 300 people representing 54+ nationalities. Powered by our commitment to care, inclusion, innovation, and collaboration, we support employees through initiatives like Employee Resource Groups (ERGs), mentorship programs, and more. Here are just a few examples of the ERGs fostering connection: LGBTQIA+ Network – Advocating for inclusivity and championing allyship. Bookclub – Encouraging cultural exchange, fostering discussion, critical thinking, and team engagement through shared reading and analysis. Blacklane Cares Leave – Spend up to eight paid hours annually volunteering for causes meaningful to you. We welcome—and actively support—employees to create or join the communities that are important to them. Why join us in a Blacklane on the fast lane? Fair Pay & Shared Success → We provide good salaries and automatically enroll all employees in our Virtual Stock Options Plan (VSOP). Your shares vest over four years, with full transparency on their value growth through Ledgy. Blacklane Mystery Rides → As a valued contributor to our success and growth, we are pleased to offer you one complimentary ride per quarter to experience and evaluate our chauffeur services firsthand. Learning & Development at Blacklane → We value the person beyond the CV, which is why we are committed to your continuous growth—not just for the company’s success, but for your own professional development. We believe that when our employees stay ahead, so does Blacklane. That’s why you have free access to thousands of online courses on LinkedIn Learning, tailored to your career aspirations, along with a unique learning experience designed for those stepping into leadership roles. The way to your heart goes through your stomach? → If so, we’ve got something special for you, in Berlin: our weekly homecooked, quality office lunches. Not only do they fuel your mind and body, but they also provide the perfect opportunity to connect with colleagues from all departments, fostering stronger bonds beyond your own division. Your Health, No Compromise → with Nilo Health We prioritize both your mental and physical well-being because a healthy team creates a thriving culture. That’s why we offer access to the Nilo Health platform for mental wellness - available for you to use as you choose. Social responsibility → We Care: Learn more about Miracle flights and the meaningful impact behind our commitment to making a difference. Share your ideas with Blacklane’s C-Level - Our approachable C-Level team values your insights. Once a month, they host a consultation day where you’re invited to share your ideas, feedback, and constructive criticism—because your voice shapes our future. Thrive Together: Our Hybrid Office Culture: At Blacklane, we believe in the power of in-person collaboration and connection. That’s why we follow a hybrid, 3 office day focused approach. This supports both our fast-paced growth and the building of strong, engaged teams. We know everyone’s situation is unique, so our hybrid setup still allows for flexibility when needed. If you’re excited about contributing in a dynamic, people-driven environment and value both teamwork and personal accountability, we’d love to hear from you! Do you have any questions or want to know more about our way of working? Feel free to contact us. We’re happy to connect! We place great value on equal opportunities. Therefore, we welcome everyone to apply! Curious to see us in action? Let’s go! → Watch us on Amazon Prime → Listen to our CEO in Startup insider episode #1643 → Blacklane is backed by investors who share our vision: Mercedes-Benz Mobility, Tasaru, Sixt, the Al Fahim Group, Alstin Capital, RI Digital Ventures, and b2venture

Responsibilities

Design, build, and maintain end-to-end machine learning systems for various use cases while collaborating with cross-functional teams. Establish best practices for experimentation and model evaluation to drive scalable data science solutions.