Do you want a job as a data scientist at Uber? Great! You are at the right place.
Uber Technologies Inc. (Uber) offers various services- enabling customers to reserve a vehicle and driver to transport them (similarly to a taxi), food delivery via Postmates and Uber Eats, package delivery, courier services, and freight transportation.
Let us first see who is a data scientist, what roles they perform, and what responsibilities they take care of in general.🏌🏻
Who is a Data Scientist?
A technological specialist specializing in data collection, management, manipulation, and analysis is a Data Scientist. They frequently are from computer science, mathematics, statistics, and business backgrounds.
Roles and Responsibilities
Data scientists at Uber are responsible for the following:
✔️Create statistical models and analyses and use them to address current issues.
✔️Using Uber's extensive datasets and providing helpful data insights and solutions.
✔️Using a command of statistical analysis, including confidence intervals, descriptive statistics, correlation, and regression.
✔️Creating SQL queries and designing metrics to produce reports.
✔️Own product development experiments and collaborate with engineering, design, and product teams throughout the product development cycle.
Salary and Perks
Salary
Average Annual Salary
₹ 32,40,000
Estimated In-hand Salary
₹ 1,97,865 - ₹ 2,05,674/month
**The salary figures mentioned in the above table are subject to change.
Perks
Uber provides assistance programs, monthly wellness reimbursements, and complete healthcare.
The employees can set aside up to 15% of their qualified income through Uber's employee stock purchase plan.
Uber offers significant paid sick days, local holidays, vacation days, and voting days. After every five years of service, each employee is given a four-week vacation.
Everyone has access to meals and snacks at Uber workplaces all the time.
Uber offers paid parental leave, flexible work schedules, access to Cleo's benefits program, and more.
Throughout the month and on special occasions, the employees receive credits for Uber Eats and Rides.
Skills and Experience Required🧑💻
The basic qualifications required are as follows:
Masters or undergraduate degree in mathematics, economics, bioinformatics, statistics, engineering, computer science, or a related quantitative subject (if you have an M.S. degree, then a minimum of 2+ years of industry experience is necessary and if you have a bachelor's degree, then, a minimum of 2+ years of industry experience as a product analyst is necessary).
Advanced SQL knowledge.
Basic comprehension of statistical techniques and experimental design (such as A/B experiments).
The capacity and expertise to draw insights from data and to summarise lessons learned or takeaways.
Excel proficiency and knowledge of dashboarding and data visualization (i.e. Tableau, Mixpanel, Looker, or similar)
The preferred qualifications are as follows:
Advanced degrees in mathematics, economics, statistics, engineering, computer science, operation research, machine learning, or another quantitative subject.
Strong experience creating time-series predictive models using traditional and cutting-edge machine learning techniques.
5+ years of industry expertise in product analytics for consumers.
Ability to manage relationships with partners who have both technical and non-technical backgrounds and to successfully communicate with them.
Ability to address complex business problems that involve many product/project areas and teams.
Strong judgment, critical thinking, and decision-making skills.
Preparation Strategy
To start preparing to become a data scientist, first, you must fulfill the skills and experience required in the previous section. After acquiring the required skills, knowledge, and experience, you should start building projects showcasing your Data Science skills. Once you have built a few projects, share your projects with others on GitHub.
Now comes the next phase, the Uber selection process.
The first interview stage is a 15 or 30-minuteinitial screening with a recruiter or hiring manager. The next challenge is an Uber take-home contest. SQL queries, experimental/business intelligence queries, and data analysis queries are all covered in the assignment. A 45-minute technical phone screening follows the take-home test. The on-site interview panel comprises five distinct interviewers and comes after the technical screen.
A glimpse of various rounds.
First round- The first stage is the initial screening. After you submit your application, a hiring manager or recruiter will call you for a phone interview. This interview aims to evaluate your general background, the team, and the job function, with the possibility of a brief technical discussion.
Second round- The second stage is the Uber Take-Home Challenge. After completing the preliminary phone screening, you will receive a take-home challenge with a one-week deadline. The assignment is divided into three parts:
SQL and Analytics: You'll be provided an example Uber problem with a schema. The question will ask you to write SQL queries to address different analytics issues.
Qualitative Section: General inquiries about metric evaluation and experimental design are included in the qualitative part.
Modeling: It is an exercise of the practical application of predictive modeling.
Third round- The process then moves on to the technical interview with a data scientist over the phone. Case studies involving Uber are frequently asked in this interview, and an open-ended response is desired. Your ability for critical thought and problem-solving will be put to the test here. Expect to receive machine learning problems like feature selection and model development emphasizing actual Uber challenges. You can expect an inquiry about the product if the role is more analytics-focused.
Fourth round- The on-site interview comes after clearing the technical screening. There are five or six rounds of the on-site interview, each lasting 45 minutes. Whiteboard coding, project discussions with team managers and data scientists, business case studies, and statistical concept discussions are all included in this all-day interview.
Typically, the panel looks like this:
📌There will be an interview with a data scientist where you will be given a few open-ended business intelligence and analytics problems and a question about statistics and probability.
📌There will be a discussion about the team during the hiring manager interview. Make sure you inquire thoughtful questions.
📌There will be a technical interview with a data scientist about machine learning. Modeling ideas and machine learning design questions are covered in this interview.
📌A 45-minute data scientist interview will include writing SQL code or using algorithms. If the position is in the analytics division, then SQL is asked, and if it is in the machine learning division, then more focus on algorithms is there.
You may refer to the Uber preparation guide to learn more about the recruitment process.
How to Become a Data Scientist
1️⃣Review the Basics of Data Science
It is essential to brush up on your Data science fundamentals, such as:
Analytical statistics- This refers to the analysis of data and the correct and unbiased presentation of the results using statistics.
Programming- Key competencies used in many areas of data science include writing code and creating software.
Visualization of data- Data is presented in tables, graphs, charts, and dashboards so that technical and non-technical people can quickly and easily understand it.
Machine learning - constructing computer software that learns from the information provided and uses that information to alter its operations in response to various circumstances.
2️⃣Enhance the Technical Skills
A data scientist should be proficient in Python and generally knowledgeable in one or two other languages, such as R or SQL.
Data miningis acquiring relevant and reliable data from various company divisions or sectors and frequently from outside sources.
A crucial talent for data scientists is using visualization tools like Tableau, Microsoft Excel, and Google Charts to display data understandably.
Data analysis is searching through and examining structured data to identify trends and other interesting features that can be used to generate practical knowledge and commercial prospects.
Natural Language Processing (NLP)is a subfield of machine learning extensively employed in developing virtual assistants like Apple's Siri and Amazon's Alexa.
Algorithms are collections of rules that carry out calculations and solve issues. AIcan be used to train them to make automated decisions.
Data gathering, wrangling, cleaning, and other preparatory processes are considered data engineering abilities.
3️⃣ Familiarize With the Data Science Tools
Apache Spark- It is a machine learning and analytics engine for big data processing and engineering.
Tableau - It is a database-connected tool for data visualization that enables users to design robust and diverse visual representations of their data.
SAS- It is a collection of statistical software tools for managing, analyzing, forecasting, and visualizing data.
MATLAB- It is a programming language for building models and developing algorithms.
Python- 75% of data scientists use the readability-focused programming language python. For statistical programming, 47% of data scientists use R.
Consider applying for a data science internship to enhance your studies with practical experience.
5️⃣Build Your Network
When you initially enter a field, developing your network is a fantastic method to get a foot in the door. It will assist you in strengthening your abilities, meeting new people who share your interests, working on passion projects, and ultimately locating employment.
LinkedIn- If used properly, LinkedIn is a helpful tool. Always stay in touch with the people you meet in the industry, even if it's only someone you met at an event.
Online Communities- Professional and amateur communities can freely discuss ideas on websites like Discord and Reddit.
Meetings and Conferences- Start going to conferences and meet-ups as soon as possible.
Career Path of a Data Scientist
Junior Data Scientist
Junior data scientist is the starting position. It is regarded as the entry-level where a candidate joins after completing the selection process.
Data Scientist
Data scientist level is achieved typically after getting 1 to 3 years of experience as a junior data scientist. The data scientist should be an expert in data analysis, analytical skills, research, python, R, SQL, machine learning, communication skills, and teamwork.
Senior Data Scientist
Senior data scientist level is usually achieved after 3 to 5 years as a data scientist. A senior data scientist understands the overall business picture. They have expertise in data analytics technologies, maintain budgets and prevent fraud.
Principal Data Scientist
Experience of ten or more years is required to become a principal data scientist. This position is the most experienced among all data science team members.
Note⚠️ - The career path of a data scientist may vary a little from company to company.
Step 2: Search for the ‘data scientist’ role in the ‘search open roles’. Set location to any city in India.
Step 3: Select the role ‘Data Scientist II’ role.
Step 4: Click the ‘Apply Now’ button and fill out the application form.
Resources For Preparation📓
The following section talks about the resources available for preparation for a data scientist at Uber.
Interview Resources
Here is a list of resources available to help you ace the data science interview in a structured manner.
Interview Bundle - Uber- complete bundle for you to ace your interview at Uber, including commonly asked interview questions and interview experiences.
Interview Puzzles- curated a list of top interview puzzles asked in various companies.
Data science/machine learning- a complete guide for you to start learning data science and machine learning for the interviews.
Interview Questions- a complete top interview questions bundle on different topics.
Problems- ace your coding skills by solving more and more coding questions.
Uber offers various services- enabling customers to reserve a vehicle and driver to transport them (similarly to a taxi), food delivery via Postmates and Uber Eats, package delivery, courier services, and freight transportation.
What role does a data scientist play?
The role of the data scientist is to collect, interpret and manage data and solve complex problems using analytical, statistical, and programmable skills.
What are the various responsibilities of a data scientist at Uber?
The various responsibilities of a data scientist at Uber include specializing in data collection, management, manipulation, and analysis. Data scientists at Uber also create statistical models and SQL queries.
What is the eligibility for the data scientist at Uber?
A Master's or undergraduate degree in mathematics, economics, statistics, engineering, computer science, or a related quantitative subject along with advanced knowledge of SQL and machine learning is required to be a data scientist at Uber.
What is the top position in the career map of a data scientist?
The topmost position is of a principal data scientist with nearly 10+ years of experience.
Conclusion
In this article, we explored various things related to the data scientist at Uber, leaving no stone unturned. We explored the roles and responsibilities, salary and perks, skills and experience required for the role, preparation strategy to become a data scientist at Uber, and the career path of a data scientist at Uber.
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