Your Role
● Work closely with business teams to identify, define, collect and track key business metrics and inform strategic decisions
● Design and run data-backed experiments to improve process efficiency and create systems to capture them
● Tool and fetch data required to conduct business analysis, build and automate reports and dashboards to monitor business performance and generate business insights
● Collaborate with functional leaders in Marketing, Procurement, Assortment strategy and Operations to build & execute data-driven forecasting models/methods.
Qualification
- Bachelor's in Engineering, Mathematics or Statistics from a reputed institute
- 2+ years of experience in a relevant role
Required Competencies
- A sound ML engineer combines critical pieces of engineering with the reality of a data scientist's life.
- Ability to architect ML engineering pipelines and tools.
- Ability to experiment with different metrics to solve complex problems
- Exceptional communication skills (Written/Verbal)
- Ability to Independently plan and Execute deliverables
- Ability to multitask and work on a diverse range of requirements
Technical Capabilities
- Must have hands-on experience in Python and Scala.
- Experience in building end-to-end ML and data science solutions at a platform level is required.
- Ability to apply software engineering best practices and rigour to ML pipelines, including automation, CI/CD , etc. Must be capable of working with AWS and MLOps tools . Experience in integrating monitoring solutions in ML applications is also required.
- Experience training large neural networks with machine learning frameworks like PyTorch or TensorFlow. Hands-on experience managing or provisioning GPU/CPU clusters or other large-scale cloud or Linux/Unix systems is necessary.
- Must be able to identify and evaluate new technologies that can improve machine learning solutions' performance, maintainability, and reliability.
- Capability to preprocess structured and unstructured data, present information using data visualization techniques, propose solutions and strategies to business challenges, and collaborate with engineering and product development teams.
- Most importantly, the ability to learn and embrace the generalist aspect of the role to pick tasks in ML, Software Engineering, Data Engineering, DevOps or Data Science.