Sphere collaborates with clients to transform their organizations by embedding technology and efficient processes into every aspect of their operations. We empower businesses to achieve lasting competitive advantage through innovation and expert guidance. At Sphere, we prioritize people and innovation to build a better future through technology.

We are seeking a Principal Data Scientist to join our team and work with a financial services client to enhance their offerings. In this role, you will develop data-driven solutions that personalize user experiences, optimize business outcomes, and drive financial inclusion for millions of users.

Responsibilities: 

• Design and implement advanced machine learning models to support strategic objectives, such as enhancing user personalization, analyzing transactional data for credit insights, and optimizing financial performance metrics.  
• Development and performance of data-driven solutions that influence core business outcomes, including customer retention, engagement, and revenue growth.  
• Utilize a wide range of data sources, including behavioral patterns, web activity, credit data, and financial transaction records, to create impactful features and insights.  
• Work closely with cross-functional teams to ensure seamless integration of data science initiatives into business operations, fostering continuous improvement and adoption.  
• Develop and maintain best practices for machine learning pipelines, establishing robust processes for scalability and operational efficiency.  
• Collaborate with infrastructure teams to deploy, maintain, and monitor predictive models in production environments.  
• Present complex data insights and model findings in a clear and concise manner to diverse audiences, bridging the gap between technical and non-technical stakeholders.  

Requirements:

• 10+ years of experience in Data Science and Machine Learning.
• 5+ years of experience with Git, GitLab, GitHub, or similar version control tools.
• Experience with SQL and programming languages such as Python or R.
• Experience with AWS Machine Learning Services and tools like S3, Glue, Redshift, SageMaker, and Airflow.
• Familiarity with libraries like NumPy, SciPy, Pandas, Scikit-Learn, and Matplotlib.
• Knowledge of data warehousing, data modeling, and machine learning methodologies.
• Expertise in techniques like propensity modeling, lead scoring, clustering, recommendation systems, and causal inference.