Job description:
PURPOSE OF THE ROLE
Anheuser-Busch InBev (AB InBev)’s Commercial Analytics is responsible for building competitive differentiated solutions that improve profitability, revenue or save costs in our Marketing & Sales capabilities (assortment optimization, price & promo optimization, shelf-space, e-commerce to name a few). As a SDE you will work at the intersection of
- Application of machine learning/deep learning solutions.
- Best in class cloud technology & micro-services architecture.
- Use DevOps best practices that include model serving, data & code versioning.
As a bonus, you will build new product features from start to finish (e.g., develop & deploy new demand models served into production systems). You also can review & influence the engineering design, architecture & technology stack across multiple products, outside your immediate focus.
WHAT YOU WIL DO?
- You will work on building code that will deploy into production, using code design & style standards.
- You will document your thought process & create artefact on team repo/wiki that can be used to share with business & engineering for sign off.
- You will review code quality, design developed by your peers.
- You will significantly improve the performance & reliability of our code that create high quality & reproducible results.
- You will also develop internal tools/utils that improve productivity of entire team.
- You will collaborate with other team members to advance team’s ability to ship high quality code, fast!
- You should be able to mentor/coach junior team members to continuously upskill them.
- You will maintain basic developer hygiene that includes but not limited to, writing tests, using loggers, readme to name a few.
Skills –
Data Science, Machine Learning, Pycharm, Python
- Graduate degree (data science, statistics, applied mathematics, mathematics, economics, etc.).
- Proficiency with pandas, numpy, scipy, scikit-learn, statsmodels, tensorflow.
- Proficiency with classical approaches to machine learning, linear algebra & mathematical optimization.
- Good foundation in supervised & unsupervised ML models.
- Excellent understanding of science fundamentals (experimental design, hypothesis testing, reproducibility).
- Strong at Python coding. Exposure to working in IDEs such as VSC or PyCharm.
- Experience in code versioning using Git, maintaining modularized code base for multiple deployments.
- Experience in working in Agile environment.
- Basic understanding of any public cloud, RESTful APIs & containerization.
You should have expertise in at least one of the following:
- Good foundation in data structures & algorithms.
- Strong experience in code versioning using Git (or alike), maintaining one modularized code base for multiple deployments, automation using CI/CD pipelines.
- Experience in working in Agile environment.
- You should have at least 3 years of experience working in distributed/cloud-based environment.
- Experience in microservice architecture, domain driven design & building RESTful services.
- Experience in internal aspects of run time environments, complex libraries/dependencies, docker containerization.
- Guide & mentor team to make right technology choices. Challenges status quo with an owner’s mindset of what can break & think of proactively fixing.
- You have bias for action & make right trade-offs between engineering design & solving business needs.
- Skilled with common front-end technologies such as HTML, CSS, JS, TypeScript.
Few skills in addition that will make you stand out…
- Experience in mentoring/managing junior team members & developing their skills.
- Experience working with data scientists & helping put machine learning solutions into production.
- Contributions to OSS or Stackoverflow.
Qualification, Course, Specialization -
Academic degree in, but not limited to, Bachelors or Masters in CA, CS, or any engineering discipline. Beyond academic degrees, we give more weightage to 5+ years of real-world experience to develop scalable & high[1]quality software.
