We are looking for an experienced Machine Learning Engineer capable of working with a multidisciplinary
team to deliver the technology stack to deliver the MarSci innovation. Candidates with an interest in AI,
working with LLMs and/or ML engineers in digital analytics industry are particularly encouraged to apply

The Machine Learning Engineer holds a Masters or PhD in Computer Science or a related field
and will bring over 2 to 3 years of experience in building ML and training AI models, specializing in
machine learning, neural networks, and predictive analytics. Their expertise in AI and machine
learning is critical to developing MarSci’s innovative modeling techniques, such as interpreting
Multi-Touch Attribution (MTA) and Media Mix Modeling (MMM). They will work closely with data
engineers to optimize AI-driven insights and provide real-time recommendations.

Main Responsibilities

As part of the overall project, this Machine Learning Engineer will work on the following tasks:
1. Algorithm Development and Implementation:
•Develop models for Multi-Touch Attribution (MTA) which can shed light into complex consumer
journey challenges.
• Develop ML models for Media Mix Modeling (MMM) – combine existing ML models and innovate
model transformation for Media mix modelling.
2. AI research and development:
• Train open-source AI models in order to support the vision of MarSci for better data interpretation.
• Utilize AI models in order to define hyperparameters which will be used for Media Mix Modelling.
• Extend existing AI developments in order to support MarSci’s solution.
3. Model Optimization and Deployment:
• Conduct evaluations and fine-tuning of machine learning models to ensure optimal performance.
• Deploy machine learning models into production environments, ensuring scalability and reliability.
4. Innovation and Research:
• Stay informed about the latest advancements in artificial intelligence and machine learning to
integrate state-of-the-art techniques into the platform.
• Explore and apply emerging methodologies to enhance predictive analytics and decision-making
capabilities.
5. Agile Development:
• Employ Agile methodologies to drive iterative development, ensuring continuous delivery of
impactful features.
• Participate in sprint planning and deliver prototypes aligned with short-term milestones and the
platform’s long-term vision.
6. System Architecture and Scalability:
• Contribute to defining the technical and architectural requirements necessary to support the
platform’s growth and innovation.
• Balance the immediate project goals with strategic plans for scalability and future development.

Knowledge & Experience

Essential:
• A minimum of 5 years’ experience in a software developer role;
• Master’s or PhD in Computer Science or related field.
• 2+ years of experience in machine learning model development.
• Proficiency in Python, TensorFlow, or PyTorch.
• Strong understanding of AI systems and predictive modeling.
• Understanding of data architecture, pipelines and ELT flows/technology/methodologies.
• Experience using pipeline technologies within AWS.
• Knowledge of data modeling and statistics.

Desirable:
• Digital analytics background: Familiarity with the digital analytics industry and/or
familiarity with media mix modeling and attribution methodologies.

About You

• A degree in computer science, statistics, applied mathematics, or a related quantitative field.
• At least three years of experience in machine learning, with a strong focus on training open-source AI models for specific
applications.
• Deep knowledge of ML and AI techniques, with an ability to select the most appropriate approach for different problems.
• Proficiency in Python and familiarity with key ML frameworks such as TensorFlow, PyTorch, and Hugging Face.
• Strong understanding of data infrastructure, including Snowflake, Airbyte, and cloud-based data solutions.
• Hands-on experience with ML deployment tools and environments, such as Docker, Kubernetes, Spark, and Dask.

Desirable
• Strong familiarity of Python programming and ML frameworks (Jupiter notebook, Google
Collab, TensorFlow, PyTorch, HuggingFace, etc.)
• Very good understanding of data infrastructure (Snowflake, Airbyte, Cloud)
• Familiarity with standard ML deployment stack (Docker, Kubernetes, Spark, dask, etc.)
• Solid foundation in statistical modeling and advanced machine learning techniques,
including time-series forecasting, multi-touch attribution (MTA), and media mix modeling
(MMM).
• Knowledge of large language models (LLMs), fine-tuning methods, and prompt
engineering to enhance model performance and align outputs with marketing analytics
objectives.
• Strong programming skills in Python, R, or Julia, with a focus on building reusable
codebases for machine learning applications.

Further information on the role is available by downloading the job spec.

Application Procedure
Applicants should email [email protected] providing the following information when applying:
1. A motivation statement outlining their interest and suitability for the position.
2. A comprehensive curriculum vitae
3. The names and contact details (e-mail) of three referees.
Note:
Candidates who do not address the application requirements above will not be considered for
an interview