Lead Data Scientist

Zuhlke Engineering Ltd - Northern Quarter

Full-time (Permanent)
Northern Quarter

In the UK, we support digital innovation and embedded software. Across the group, we cover digital systems, Internet-connected devices, and physical product design (embedded and mechatronics).   

Our clients range from the largest multinationals to start-ups: what they have in common is the need for a partner who can be trusted to deliver effectively.  

Your job will involve working in small teams, communicating closely with our clients to design, develop, test, and ship systems that deliver value. You will be looking both to contribute to the company and to grow personally.  

Our reputation is fundamental to our business, so our culture emphasizes quality, reliability and respect. As a privately-held company owned by past and present employees, and run by engineers, Zuhlke takes a long-term view. For example, you will receive twenty days training per year including a budget to support activities such as conference attendance, our annual technical camp, and participation in focused technical subject teams. We expect our staff to be flexible so we will support you learning any required skills you don't have now.  

As part of our longer-term view, we believe that diversity in the workplace benefits all, we have ethical guidelines about which client industries we will work for, and we have an initiative to become carbon neutral during 2020.  

Your job will include: 

  • As a Lead Data Scientist you will take on technical leadership of customer projects, establishing the methodical approach, developing use cases with the customer, advising the customer on technical questions, as well as presenting and explaining results. 
  • You will manage the development of machine learning pipelines or develop these yourself: data preparation, modelling, method selection and validation. 
  • You will work within our cross-functional teams to create business value based on the investment in data. 
  • You will evaluate the technical and economic feasibility of machine learning solutions and operationalise concepts from successful prototypes in collaboration with Data Platform Architects & Engineers. 
  • As an expert, you will also assist with the acquisition of customer projects as part of an interdisciplinary bid team. 
  • You work collaboratively with colleagues and clients, be it onsite, in the Zuhlke office, or from home. 
  • You will guide and mentor colleagues in projects and in Zuhlke internal initiatives.  
  • You keep yourself technically sharp and get things done. 
  • You occasionally travel to other Zuhlke locations, e.g. in mainland Europe, for project work or for developing competencies across the Zuhlke group. 

Skills & Requirements: 

  • You have a degree in physics/mathematics or one of the computational sciences, or a comparable qualification.
  • You have rich experience of professional data science, implementing projects in industry and services, ideally in client facing contexts. 
  • You have good knowledge in multi-variate statistics and multi-model management. You are proficient in ecosystems suitable for data analysis (R or Python preferred), have experience in software development and are familiar with the concepts of streaming and database systems (unstructured/hadoop/relational/NoSQL). 
  • You have strong communication and leadership skills. 
  • You are motivated and passionate about supposition and hypothesis led data science, measurement error and machine learning; contributing to the future development of our capabilities in this area.

Our work is mainly at or within an hour's travel from our Manchester office.   

If you're interested in joining us, send us your CV and start your first step into realising your career goals at a company that values talent and cares about its employees.  We are an equal opportunity employer and value diversity at our company. 

We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, or disability status. 

 

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