Are you passionate about Data Science and solving complex business problems through data analysis? This is an exciting opportunity to work on cutting-edge data science projects that directly impact business strategies and customer experiences on a large scale.
As a Data Scientist Associate Sr. on the Customer Segmentation team, you will play a pivotal role in developing robust and scalable segmentation models using a diverse array of machine learning techniques. These models will be relevant to all lines of business and cover over 80 million Chase customers.
Job Responsibilities:
• Collaborate with Business Units: Understand unique needs and deliver tailored analytics solutions.
• Drive Business Outcomes: Utilize segmentation models enable the business to measure and analyze brand visibility, focus on acquiring new customers, promote new products, optimize user experience, predict and improve customer retention, and provide data-driven solutions for sales growth.
• Segmentation Models: Utilize advanced machine learning algorithms to build and refine customer segmentation models. Manage the end-to-end process from data extraction to model delivery, maintenance, and external engagement.
• Collaborative Approach: Work closely with various stakeholders across the organization to ensure our analytics solutions are aligned with business goals and deliver maximum value.
• Customer-Centric Focus: Prioritize understanding the needs and behaviors of our customers, ensuring that our analytics efforts contribute to creating more personalized and meaningful customer experiences.
• Foster Relationships: Cultivate strong relationships with internal teams and clients, demonstrating a passion for maximizing the impact of data-driven insights on business decision-making.
• Intellectual Curiosity: Exhibit a deep intellectual curiosity and a proactive approach to solving complex problems.
• Self-Starter: Act as a self-starter with robust technical knowledge, efficiently and effectively leveraging our data assets to meet business objectives.
• Project Execution: Plan, develop, and execute analytical projects both independently and as part of a team.
• Model Production: Lead the production of analytical insights and machine learning models, collaborating with product managers, end users, developers, and other stakeholders to integrate data discoveries and processes into operational capabilities.
• Team Success: Demonstrate a willingness to take on a variety of tasks, big or small, to ensure the success of the team, embodying a "can-do" attitude and a commitment to collective goals.
Required Qualifications, Capabilities, and Skills :
• Bachelors degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis, Operations Research) plus 5 years of experience.
• Good understanding of the machine learning lifecycle, including feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback.
• Experience with supervised and unsupervised machine learning techniques, including clustering, classification, regression, and anomaly detection.
• Proficiency with Python data science tools and libraries: pandas, matplotlib, plotly, bokeh, Spark, scikit-learn, MLLib, etc.
• Advanced experience in SQL and relational databases, big data technologies (e.g., Spark/S3), and cloud technologies (e.g., AWS, Snowflake).
• Expertise in machine learning operations (ML Ops) and delivering models in highly regulated environments.
• Excellent leadership, stakeholder management, communication, partnership, and teamwork skills.
• Willingness and ability to take ownership of complex modeling tasks and make sound decisions in a highly ambiguous environment.
• Highly enthusiastic about developing relationships with partners and stakeholders, demonstrating excellence in managing stakeholder relationships.
• Excellent communication and presentation skills, including the ability to create impactful PowerPoint presentations.
• Excellent collaboration skills and willingness to work with the team on a daily basis in a highly collaborative environment.
Preferred Qualifications, Capabilities, and Skills:
• Masters degree in an analytical field with 3 years of experience.
• AWS Certification: AWS certification is preferred, indicating a excellent understanding of cloud-based solutions, or willingness to pursue certification.
• Explainable AI: Experience with explainable AI techniques is a plus, enhancing the transparency and interpretability of machine learning models.
• Operationalization of Models: Demonstrated experience collaborating with engineering teams to deploy and operationalize machine learning models.
• Industry Knowledge: Familiarity with the financial services industry and its unique challenges and opportunities.
• Streamlit: Experience with Streamlit for creating interactive data applications.
Relocation assistance not supported / offered for this role.
Location: Plano, TX
Posted: Aug. 26, 2024, 2:24 a.m.
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