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ZELP Ltd

Data Scientist

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In-Office
City of London, London, England, GBR
In-Office
City of London, London, England, GBR

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About Zelp

ZELP is an agricultural technology company committed to creating a sustainable future for agriculture - one that meets the needs of present and future generations, promoting human and environmental health, animal welfare, and social and economic equity.

Our first focus is to develop and scale technology that measures and mitigates methane emissions in the livestock industry. Today, 1.3 billion people rely on the industry for their livelihoods and food security. However, it is the single biggest human-driven source of methane emissions globally, emitting more than both the oil & gas industry and the coal industry.

The deployment of our technologies on a global scale has the potential to drive half the global methane reduction needed by 2030, and to greatly transform the beef and dairy industries.

ZELP was an inaugural winner of the Terra Carta Design Lab, and has received funding and support from the European Commission, Innovate UK, The Global Methane Hub and the Gates Foundation. 

About the role

ZELP is seeking a highly motivated and skilled Data Scientist to join our growing team. The ideal candidate will possess a strong technical foundation in data analysis, machine learning, and statistical modeling, coupled with excellent communication and collaboration skills. The Data Scientist will play a key role in extracting actionable insights from complex datasets, driving data-driven decision-making, and contributing to the development of innovative solutions that address critical business challenges.

Key Responsibilities:

  • Collaborate with stakeholders to identify and understand business needs, translating them into data-driven solutions.
  • Design, implement, and maintain robust data pipelines for collecting, cleaning, transforming, and analysing large datasets.
  • Perform exploratory data analysis (EDA) to uncover trends and patterns.
  • Develop and apply advanced statistical and machine learning models to solve business problems, including forecasting, segmentation, optimization, and predictive modeling.
  • Conduct rigorous data analysis, interpret results, and communicate findings to technical and non-technical audiences in a clear and compelling manner.
  • Develop and maintain interactive dashboards and visualizations to effectively present data insights and support decision-making.
  • Stay abreast of emerging technologies and industry best practices in data science, continuously exploring opportunities for innovation and improvement.
  • Contribute to the development of a data-driven culture within the organization, promoting the value of data-informed decision-making.

Required Skills and Experience:

  • 4+ years of experience in data science, analytics, or a related field.
  • Proven experience working with large datasets and statistical modeling.
  • Proficiency in programming languages such as Python or R, with a strong understanding of object-oriented programming concepts.
  • Experience in designing and implementing ETL pipelines, data warehousing, and data modeling.
  • Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
  • Expertise in statistical modeling, machine learning algorithms, and data mining techniques.
  • Familiarity with data visualization and reporting tools, such as Tableau, Power BI, or Looker.
  • Familiarity with cloud platforms (e.g., AWS, Azure, GCP) is a plus.
  • Strong analytical and problem-solving skills, with the ability to translate complex data into actionable insights.
  • Excellent communication and presentation skills, with the ability to effectively communicate with both technical and non-technical audiences.
  • Collaborative mindset and ability to work effectively within cross-functional teams.

Interview Process

  • HR Call
  • Introductory Call with Hiring Manager
  • Short Take-home exercise & Technical interview
  • Offer
HQ

ZELP Ltd London, England Office

London, United Kingdom, N7 6LR

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