Job description

SEED AI at a Glance:

SEED AI is a young and dynamic artificial intelligence and IT consulting company. Its mission is to unleash the potential of data for all and do so by providing all services organizations need to help them grow. SEED AI provides support and guidance to identify potentially high value generating projects ideas. We develop and implement tailored machine learning solutions to modify and optimize our clients’ process.

We strongly believe data is a strategic asset which has the potential to create strong competitive advantages. We view machine learning as a tool to transform processes, products and services using data. We apply the parsimony principle using these tools and focus on impact, understandability, flexibility and actual use of those tools by end users.

SEED AI values are centered on goodwill, impact, and leadership by example. Our employees are passionate about data, focused on client needs and committed to growth. SEED AI offers a fast-paced environment that gives space to try, learn and develop projects with high impact. It offers its collaborators the chance to contribute to the structuring and growth to data-related usage in the society and economy.

Responsibilities:

• Own and deliver high impactful data-related projects.
• Translate client needs into data and systems requirements.
• Evaluate the complexity of a project through a high-level audit of available data.
• Conduct recommendation-driven descriptive data studies aligned with clients needs.
• Develop simple, understandable and impactful machine learning models.
• Present results to key stakeholders in a clear and concise fashion.
• Keep constant and genuine discussions with stakeholders at all stages of projects.

Qualifications:

• Proven delivery of successful data analysis and modelling projects. 
• Proven coding ability with Python and its data analysis packages (NumPy, Pandas, ScikitLearn, PyTorch, TensorFlow, Plotly, Matplotlib etc.).
• Practical experience with statistical learning (generalized linear models, tree-based models, clustering, variance decomposition, variable selection, sampling and bias theory etc.).
• Understanding of software development and data-related technologies and concepts including REST API, MapReduce, Spark, NoSQL, Docker, Kubernetes, etc.
• Strong communication and presentation skills.
• Degree in a quantitative field such as Statistics, Mathematics, Computer Science.