| Company: | ABINBEV |
|---|---|
| Job Role: | Manager - Data Science Analytics. |
| Experience: | (2-3 years) |
| Vacancy: | 10+ |
| Qualification: | BE/B.Tech, ME/M.Tech, MBA |
| Salary: | Rs.90,000/- |
| Location: | Bangalore. |
| Join us on Telegram | Click Here |
| Apply Mode: | (Online) |
| Deadline: | Not Mentioned |
As a brewer, InBev was formed by the merger of Belgian-based Interbrew and Brazilian brewer AmBev in 2004. Anheuser-Busch InBev (abbreviated AB InBev) began as an independent company until it was acquired by Anheuser-Busch in 2008. More than 130 countries were served by InBev, which had operations in over 30 countries. In 2006, its market capitalization was €30.6 billion, and its net profit was €3.2 billion.
InBev and Anheuser-Busch agreed on July 13, 2008, to form a new company called Anheuser-Busch InBev. The combined company board will have two seats for Anheuser. On March 13, 2009, InBev completed the divestiture of its company that imported Labatt's beer, another InBev brand, into the United States in order to receive antitrust approval in the United States.
As a result of the all-cash deal, which created the world's largest brewery by joining Budweiser, Michelob, Bass and Brahma with Stella Artois, Bass and Bass, the world's largest brewer is now the world's largest. With combined yearly sales of $36.4 billion, the two companies are likely to surpass their predecessor, London-based SABMiller.
SABMiller was acquired by Anheuser-Busch InBev for £69 billion (US$107 billion) on October 10, 2016. On a global level, SABMiller stopped trading. After purchasing Molson Coors, the newly formed company sold MillerCoors to Molson Coors and Asahi Breweries acquired many European brands from the new company, now Anheuser-Busch InBev SA/NV.
Purpose Of Role:
- Anheuser-Busch InBev (ABInBev)’s Middle America’s Commercial Analytics is responsible for building
- Competitively differentiated sales solutions that improve profitability, revenue, or cost efficiency
- (Assortment optimization, price and promo optimization, shelf space optimization, and e-commerce.
- Just to mention a few). Your work as a senior data scientist will involve intersections between (a) methods affecting the realm of
- Data analytics using machine learning/deep learning or statistical/econometric modeling, (b) leveraging cloud data at its best,
- In addition, (c) identifying business requirements using deep insight from technology stacks. As a result of your efforts
- Developing business solutions requires not only implementing them, but also measuring them
- Your work is having a positive impact.
Role expectations:
- Working on building data science solutions for solving business problems will require a high level of rigor
- There are many problems related to assortment optimization, price and promotions, demand planning, and supply planning
- To name a few, we have logistics, omni-channel analytics, etc.
- This activity consists of creating artefacts that can be used for business purposes and documenting your thought process
- Submitted for signature.
- You will be able to write code that generates reproducible results and is designed in accordance with the design of the team
- It is a practice.
- Your peers will develop methodology and code that you will review.
- Assist fellow team members in creating high quality solutions and collaborate with other team members to ensure team success
- You need to move quickly. Continually upskill your junior team members by mentoring/coaching them.
- Create KPI dashboards for tracking the quality of your solutions and evaluating what you do
- Value.
- Your results will be communicated to the business in a straightforward, explainable way that drives decision-making
- The process of making.
- Masters or PhD in any of the following areas: Mathematics, Physics, Chemistry, and Computer Science
- Our main focus is on physics, statistics, economics, computer science, information technology, and organizational behavior.Data scientist with at least five (Master's) or one (PhD) years of real-world experience
- Using statistics, economics, machine learning, and deep learning to solve real business problems
- There are a number of master expertise requirements you should meet, including the following:
- Analyzing time series or regressions based on statistical/econometric models
- Non-parametric methods, Bayesian statistics.
- Boosted and Deep Learning methods are some examples of Machine Learning/Deep Learning
- Statistical methods, neural networks, deep networks, natural language processing, reinforcement learning
- Feature Engineering, model training, and evaluation experience should be at least three years old
- Establish A/B tests (as necessary) and fine tune the models according to business needs
- feedback.
- Experience in exploring data, hypothesis formulation, data wrangling that enriches understanding
- and creating clean data for analysis.
- You have above average experience in using Python for data science, visualization, and scripting.
- In addition, you also have experience to move code into production via version controls, PEP8
- standards, writing docstrings, unit tests and creation of supporting artefacts.
- You have bias for action and make right trade-offs between analytical rigor and solving business
- needs.
Technical skills:
- You have above average experience in using Python for data science, visualization, and scripting.
- Experience with writing easily understood code along with docstrings, unit tests and supporting
- artefacts helps.
- You have worked in a large cloud database (any) and familiarity with distributed computing e.g.
- leverage Spark for large jobs.
- Experience in Github or alike tool for code versioning
- Intermediate or above expertise with SQL for querying and manipulating data.
- Few skills in addition that will make you stand out…
- Experience in mentoring/managing junior team members and developing their skills.
- Experience to adoptsoftware development best practices that scale your models into production.
- Worked in a fast-paced environment and being comfortable with ambiguity.
- Published white papers or academically peer reviewed papers that advance research. Or
- participated in open hackathons e.g. Kaggle.
Join Our What'sApp Groups Click Here
How to Apply Click on the Below link, it will redirect you to source Page and apply there.
How to Apply Click on the Below link, it will redirect you to source Page and apply there.
Apply Now Click Here
Note:
If you are facing any issues while applying jobs, please let us know by commenting below. We will solve your issue as soon as possible, Advice not to share card details pay money to anyone, and be aware from those types of traps
.jpeg)
.jpeg)
No comments:
Post a Comment