Senior Data Scientist

Senior Data Scientist

Requirements

• Advanced proficiency in establishing robust MLOps practices to automate and streamline machine learning pipelines from model development to deployment and monitoring.
• Advanced business acumen and communication skills with strong ability to interact effectively with senior business leaders and IT stakeholders.
• Superior analytical skills with deep understanding in predictive modeling, clustering, classification, and optimization algorithms.
• Expert data processing skills with experience with SQL and NoSQL databases and demonstrated comfort navigating both relational and Hadoop-based data environments.
• Advanced proficiency with MLOps Capabilities. Demonstrated ability to integrate and deploy AI/ML solutions into production environments using contemporary MLOps frameworks and cloud services.
• Highly skilled in using contemporary visualization platforms to translate complex data findings into understandable and actionable business insights.
• Capabilities in leading projects and managing teams within a data-driven, analytical environment.
• Leadership and mentorship skills with the ability to monitor and evaluate programs to ensure SLAs and business objectives are achieved.
• Excellent organizational and project management skills.
• Master’s Degree Quantitative, Analytical, STEM fields with minimum 6 years’ of experience.
• 6-8 years experience Quantitative, Analytical, STEM fields.
• 6-8 years experience Machine Learning, Deep Learning, or Artificial Intelligence.
• 6-8 years experience Programming experience in Python, PySpark, R, or Scala.
• 0-2 years experience Cloud and Big Data Technologies: one (1) year of experience with major cloud platforms (AWS, Azure, GCP) and technologies such as Snowflake, BigQuery, or Redshift.

Responsibilities

• Be accountable for analyzing large, structured, and unstructured datasets in cloud environments, utilizing services like Snowflake, AWS S3, or Google Cloud Storage.
• Lead development of advanced predictive models to drive personalized banking experiences. Develops and implements strategic quantitative analytic methods that obtain desired results, partnering with senior organizational leaders.
• Be involved in and oversee the building, testing, validating, and deploying of machine learning and AI models using cloud-based platforms such as AWS Azure, GCP, and Snowflake. Implements MLOps practices to streamline and automate model lifecycle management.
• Work independently, with minimal supervision, while serving as a subject matter expert in the area of Data Science and all related disciplines.
• Possess expert proficiency in contemporary visualization platforms such as Microsoft Power BI, Tableau, Google Data Studio, and Looker. Ability to create dynamic, interactive data visualizations that communicate complex findings and drive strategic business decisions. Skilled in translating technical and analytical data into clear, understandable visual formats for various audiences, including business leaders and stakeholders.
• Continuous Learning and Innovation: Stays updated with emerging technologies and methodologies in data science and machine learning, adapting quickly to a dynamic environment. Coaches, mentors and delegates work to less experienced Data Scientists and colleagues on the Data & Enablement team.
• Responsible for managing complex projects and data science work, providing strategic guidance and implementing tactics and project plans to complete the work in established timelines.

Preferred Qualifications

• Ph.D. in Quantitative, Analytical, STEM fields with 2+ years’ of experience.
• 9+ years experience Quantitative, Analytical, STEM fields.
• 3-5 years experience in Banking/Financial Services.
• 3-5 years experience in marketing/consumer analytics.
• 3-5 years experience in designing and implementing models for next best products or customer experience enhancements in the banking or financial services sector is highly desirable.
• 3-5 years familiarity with MLOps tools and platforms, such as MLflow, Kubeflow, or AWS SageMaker. Experience with Agile software development methodologies.
• Proficiency in modern analytics and visualization tools such as Google Data Studio, Looker, or Microsoft Power BI.
• Experience with stream-processing systems like Kafka or Spark Streaming is a plus.