Data Scientist Lead - Employee Platforms

Revolutionize the future of Employee Platforms with cutting-edge AI and Data Science! Join a dynamic team dedicated to creating innovative, cloud-centric solutions that transform client experiences and drive industry-leading advancements.

As a Data Scientist Lead in Employee Platforms, you will collaborate with a team of innovators to develop AI/ML solutions. Your work will directly impact our ability to provide exceptional service to clients by delivering cutting-edge technology solutions. Each day, you will engage in end-to-end software development, from design to deployment, in a fast-paced, cloud-native environment that values continuous learning and innovation. Your contributions will help keep our Employee Compute services at the forefront of the industry.

Job responsibilities

Develop and deploy machine learning models and generative AI capabilities. Design, code, test, and debug applications. Collaborate with cross-functional teams to achieve common goals. Keep stakeholders informed on development progress and benefits. Manage project lifecycle and software development deliverables. Solve complex problems and handle ambiguity with strong analytical skills.

Required qualifications, capabilities, and skills

Bachelors or Masters in Computer Science or related field Strong programming skills in python and knowledge of software engineering best practices Strong knowledge of basic data science libraries in Python (NumPy, pandas, scikit-learn, pyspark) Strong knowledge of the main deep-learning frameworks such as PyTorch, TensorFlow, Keras Experience with Linux and shell scripting and experience with LaTeX Solid understanding of traditional data science techniques and experience with data engineer pipelines for big data Solid knowledge of RNNs, and LSTMs models 

Preferred qualifications, capabilities, and skills

Experience with cloud-native development and deployment- Knowledge of AWS cloud services is a plus. Familiarity with project lifecycle and version control practices. Experience with machine learning algorithms on graphs. Strong ability to collaborate in a diverse, global team environment.