We are seeking a talented Lead Data Scientist/ML Engineer to join an embedded team. In this role, you will be crucial in developing and implementing machine learning solutions for several projects. Your expertise will be essential in driving the success of the company initiatives.

About the Customer

The client is the largest Google digital consulting agency in Europe, operating only in the Google cloud.

About the Project

The project involves analyzing customer payment behavior, historical data, and potentially customer communications to develop an AI-driven solution for proactive late payment mitigation.

Requirements

• 5+ years of experience in Data Science/Machine Learning
• Proficiency in Python and ML-related libraries such as Pandas, Scikit-learn, Matplotlib, PyTorch, TensorFlow, SQL, etc.
• Deep understanding of machine learning algorithms (time series analysis, clustering, classification), their limitations, and the appropriate use cases.
• Proven track record of starting machine learning projects from scratch and seeing them through from PoC to deployment in production.
• Experience with Generative AI technologies and RAG applications, including Prompt Engineering.
• Creative approach to turning data into stories, insights, and actionable recommendations.
• Sharp analytical skills, with the ability to understand business needs and propose data-driven solutions.
• Excellent communication skills, with the ability to effectively engage with clients, understand their objectives, and explain complex technical concepts.

Nice to Have

• MS or BS in computer science or related field
• Experience with GCP (BigQuery, Google Cloud Storage, Vertex AI)
• Strong experience with MLOps and setting up scalable pipelines for model development and deployment.

English level

Upper-Intermediate

Responsibilities

• Gather and prepare relevant data: Collect data from various sources, ensuring it is properly structured and ready for analysis through cleaning, transformation, and feature engineering.
• Develop predictive AI models: Design, build, and validate machine learning models to accurately predict specific outcomes related to the project's goals.
• Utilize Google AI for advanced analysis: Leverage Google AI tools to uncover hidden patterns and insights within various data types, including unstructured text data.
• Create informative visualizations: Develop clear and insightful dashboards and reports to communicate key findings and model performance to stakeholders.
• Share knowledge and facilitate understanding: Effectively present project results and methodologies, ensuring stakeholders can interpret and utilize the AI-powered insights.