About Delta Tech Hub:
Delta Air Lines (NYSE: DAL) is the U.S. global airline leader in safety, innovation, reliability and customer experience. Powered by our employees around the world, Delta has for a decade led the airline industry in operational excellence while maintaining our reputation for award-winning customer service. With our mission of connecting the people and cultures of the globe, Delta strives to foster understanding across a diverse world and serve as a force for social good. Delta has fast emerged as a customer-oriented, innovation-led, technology-driven business. The Delta Technology Hub will contribute directly to these objectives. It will sustain our long-term aspirations of delivering niche, IP-intensive, high-value, and innovative solutions. It supports various teams and functions across Delta and is an integral part of our transformation agenda, working seamlessly with a global team to create memorable experiences for customers.
Responsibilities & Primary Functions:
- Responsible for the design, development, and monitoring of ML/AI models (e.g., forecasting, optimization, propensity models, etc.) that directly address the business requirements and drive measurable CX improvements
- Collaborates with COE team and business owners to take a data-driven approach in identifying key customer pain-points, uncover insights, and develop techniques to address these issues
- Demonstrates detailed understanding of data science techniques and machine learning algorithms, calibrating and enhancing existing models, and monitoring model performance
- Write clean, robust, modular Python code for developing ML models
- Leverage Gitlab to store, maintain, and collaborate on project code bases; conduct peer review code of fellow data scientists on the team
- Experience in running experiments applying ML techniques such as Causal ML for evaluating impact of events such as new product launches, failure events, customer pain points, etc.
- Leverage emerging technologies and identify efficient and meaningful ways to deliver meaningful insights to the business
- Presents analysis to business users in a digestible way
What you need to succeed (minimum qualifications):
- Bachelor’s degree in data science, statistics, mathematics, computer science or engineering field
- 3+ years of relevant experience in data science/machine learning, working with traditional ML use-cases that use tabular data
- Hands-on experience in business case delivers with customer/consumer level data
- Knowledge and experience in implementing Causal ML for experimentation use-cases
- Strong grasp of core statistics concepts
- Thorough understanding of key machine learning algorithms (e.g., supervised and unsupervised learning techniques, while avoiding superficial application of algorithms)
- Expert proficiency in SQL and Python for data science/machine learning
- Proficiency in using code repository management applications (i.e., GitHub) for storing, maintaining, and collaborating with others on project code
- Experience in designing and implementing ML/AI models for cloud-based solutions on leading cloud providers (i.e., AWS, Azure, etc.)
- Embraces diverse people, thinking, and styles
- Consistently makes safety and security, of self and others, the priority
Behavioral Competencies:
- Ability to produce high quality results, work in a collaborative environment by embracing diverse perspectives and with a solution-based approach.
- Adapt communication clearly and concisely based on team dynamics and expresses thoughts & ideas effectively.
- Ability to engage effectively with peers and stakeholders to build trust and reliable working relationships.
- Ability to understand business processes, implement innovative solutions, guide juniors on continuous improvement by constantly updating oneself on current technology & trends.
- Inquisitive to understand customer and business expectations while bringing more valuable contributions on technical solutions.
What will give you a competitive edge (preferred qualifications):
- Personal Git repository of ML projects executed on public datasets and/or leveraged in data science competitions
- Knowledge of additional machine learning areas such as Generative AI – LLMs, RL, Optimization, etc.
- Knowledge of ML lifecycle management platforms such as ML Flow, AWS Sage maker, etc.
- Knowledge of ML Ops activities
- Self-motivated and take pride in building great experiences for users, whether they are employees or customers
- Resourceful in finding the data and tools you need to get the job done
- Not afraid to ask for help when you need it, or help teammates when they need a boost
- Intensely curious about finding a solution to the pain-points of our customers along the entire travel experience
