Senior Data Scientist

Job Description 

So, what will your new role look like?    

You will be part of a product squad, working to deliver value to our ShareGate customers using AI and advanced analytics. You will be digging into large datasets to find relevant insights, supporting the product teams by leveraging our data to build better products! You will also contribute to building AI features, from ideation to monitoring the feature in production! You will also contribute to Workleap’s data science expertise by fostering the business's data culture.  

Your responsibilities will include:

• Developing your knowledge of ShareGate product strategies and identifying opportunities to leverage our data in our products. 
• Collecting, cleaning, exploring and managing large quantities of data, both structured and unstructured, internal, and external, to build different analysis to guide product decisions.  
• Developing, maintaining and supporting the full lifecycle of AI models in our products (including ML Ops / LLM Ops). 

What does your future team look like?   

You will be joining Workleap’s Data & Analytics department, with 12+ experts in various areas of data science. You will work closely with the products’ teams (product managers, designers, and developers) to build value for our customers using AI and advanced analytics. You will also work alongside the team’s data engineers, data analysts and other data scientists. 

Here are a few examples of technologies used by the Data & Analytics department: Python, SQL, Azure Synapse, Azure Data Factory, Azure Machine Learning, Mixpanel, PowerBI. 

What are the next challenges waiting for your team? 

The next challenge for the team is to identify the best AI opportunity for ShareGate and bring this opportunity into production!  

Qualifications 

• Proven experience in artificial intelligence: knowledge of machine learning concepts and the main models (LLM, prediction, segmentation, anomaly detection, natural language processing, etc.); 
• Programming experience (Python, SQL);   
• Basic knowledge of data collection and storage techniques;     
• High level of autonomy;   
• Work experience in building AI for SaaS products; 
• Knowledge of ML Ops and/or LLM Ops principles;  
• Interest in and experience with data preparation and analysis;    
• Interest in the company’s areas of business (business acumen);   

Assets: 

• Experience working with cloud infrastructures; 
• Experience with Azure Machine Learning. 

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