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The Andela Standard: Network Entrance Assessments

Machine Learning engineers are instrumental in crafting, enhancing, and sustaining machine learning applications. Working collaboratively across teams, they ensure the effective integration of machine learning components, optimize application performance, and resolve intricate challenges. Successful candidates exhibit proficiency in machine learning, demonstrate a deep understanding of data science architecture, and showcase problem-solving skills in dynamic software environments.

1. Qualifications

Qualification criteria:

  • A minimum of 4 years of professional experience in a Machine Learning Engineer role (excluding intern and volunteer experience).
  • A bachelor's degree in a technology-related field or equivalent work experience.
  • Excellent spoken and written English communication skills.
  • Proficient in machine learning with a solid understanding of data science principles.
  • Hands-on experience with machine learning technologies, including data preprocessing, model development, and familiarity with machine learning libraries.
  • Strong problem-solving skills, attention to detail, and a results-oriented mindset in the realm of machine learning engineering.
  • A commitment to continuous learning and adapting to evolving technologies in the machine learning domain.
  • A collaborative and team-oriented approach to projects.

2. Skill Validation

This qualification assessment tests the candidate's knowledge of Machine Learning Engineering fundamentals. It's composed of Q&A and code challenges that evaluate the candidate's theoretical knowledge and hands-on skills. A candidate is approved in this test if all challenge level cut-off scores are met.

Machine Learning Fundamentals

This challenge is designed to assess the knowledge and skills of mid-senior engineers in Machine Learning. It consists of questions about Machine Learning fundamentals, including concepts and features, as well as practical questions exploring scenarios Machine Learning engineers typically find in real-world applications.

SQL

This challenge tests candidate knowledge of SQL and associated best practices. The question difficulty ranges from basic to intermediate, so the challenge should be suitable for any experienced SQL practitioner. Most of the questions are derived from SQL certifications and some inspired by frequent SQL interview questions.

Pandas Code Challenge

This challenge involves the use of Pandas to merge two World Factbook datasets and select countries with the highest obesity rates. Candidates can merge CSVs and perform fundamental operations on dataframes such as renaming, resetting indices, merging, sorting and selecting.

3. Technical Interview

This assessment is designed to test the candidate's practical knowledge of Machine Learning Engineering. Candidates who pass this challenge are able to work with Machine Learning to implement Machine Learning models.

During the assessment, candidates will tackle a hands-on problem tailored to real-world scenarios in Machine Learning Engineering. Their solution will be first verified by a battery of automated test cases to ensure it meets the functional requirements as well as industry standards. Then, our experienced interviewers will step in to assess the overall efficiency of the candidate's solution.

Assessment Outline:

  • Machine Learning Challenge (Option 1):
    • This is an intermediate-to-advanced-level problem that asks the candidate to write a function to remove outliers from a data set. They'll need to remove the outliers from the dataset given a cutoff passed as a parameter to the function together with the original dataset.
  • Machine Learning Coding Challenge (Option 2):
    • The first challenge is is a basic Pandas problem that asks a tests basic skill in grouping and selecting. Given a simple two-column table, the candidate should find groups in columns a that have a specific value in any column b within that group.
    • The second challenge is a basic NumPy challenge which tests comfort with arrays and masking. The candidate should chop the tops off an array in NumPy.

Build Your Machine Learning Dream Team

Asive D.
Data Scientist

Experience:

10+ years

Availability:

Full-time
Senior Data Scientist with 10 years of experience in data analytics, data engineering, machine learning, and deep learning. Highly adept in
using analytics to drive insights to help organizations make informed decisions. Experienced in developing high-performance models that
increase efficiency and accuracy of results.
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Expert in
Cristina Z.
Data Scientist

Experience:

9+ years

Availability:

Full-time
Cristina is an accomplished Data Scientist with 8 years of expertise in developing impactful data-driven solutions. Her proficiency encompasses data analysis, Data Science, SQL, Python, applied statistics, and Data Engineering, complemented by a good grasp of AWS. She possesses a strong command of diverse facets, ranging from data modeling, data normalization, statistics, and mathematical concepts to Machine Learning.
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Expert in
Leandro B.
Data Engineer

Experience:

10+ years

Availability:

Full-time
Leandro is an experienced Data Engineer with over ten years of expertise spearheading business intelligence and data initiatives across diverse sectors. He is proficient in data architecture and engineering utilizing Azure and Power BI and has hands-on experience in Machine Learning, SQL, R programming, and data visualization. With a comprehensive grasp of Microsoft architectures, Leandro specializes in end-to-end design and implementation.
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Expert in
Matias A.
Data Scientist

Experience:

10+ years

Availability:

Full-time
Matias is a Data Science Engineer with 10+ years of professional experience delivering creative solutions for social impact projects. Matias's experience includes working at IBM research as a Machine Learning engineer, collaborating with IBM's Yorktown Heights research lab, co-founding a startup that develops research-backed cognitive games for the elderly, and working on several projects that use Machine Learning to innovate in the healthcare sector.
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Frequently Asked Questions

Where can I hire Machine Learning Engineers?

Andela is a premier global talent marketplace that connects companies with highly skilled Machine Learning Engineers from around the world. With a rigorous certification process and focus on quality talent, Andela provides access to a diverse pool of experienced Machine Learning Engineers ready to join your team.

How are Andela Machine Learning Engineers different?

Andela Machine Learning Engineers stand out for their exceptional technical skills, work ethic, and cultural fit. Through an AI-driven platform, rigorous certification process, and professional matchers, Andela ensures companies are connected to highly skilled professionals who are not only proficient in Machine Learning technology but also possess strong problem-solving abilities, excellent communication skills, and a passion for continuous learning. Andela’s developers are also trained in remote collaboration and agile methodologies, enabling seamless integration into your team.

How long does it take to hire Machine Learning Engineers through Andela?

Andela’s streamlined hiring process ensures you can access top Machine Learning Engineers quickly. Once you submit your hiring request, Andela typically provides you with a curated list of pre-qualified candidates within seconds and you can hire within 48 hours – after conducting interviews and making a decision. With Andela’s global talent pool, you can scale your Machine Learning team rapidly, without compromising on quality.

What are the minimum requirements of all Andela Machine Learning Engineers?

A minimum of 4 years of professional experience in a Machine Learning Engineer role (excluding intern and volunteer experience).A bachelor's degree in a technology-related field or equivalent work experience.Excellent spoken and written English communication skills.Proficient in machine learning with a solid understanding of data science principles.Hands-on experience with machine learning technologies, including data preprocessing, model development, and familiarity with machine learning libraries.Strong problem-solving skills, attention to detail, and a results-oriented mindset in the realm of machine learning engineering.A commitment to continuous learning and adapting to evolving technologies in the machine learning domain.A collaborative and team-oriented approach to projects.

What are the typical responsibilities of a Machine Learning Engineer?

Design, build, and maintain scalable and efficient machine learning applications.Collaborate with cross-functional teams to ensure the seamless integration of machine learning components into the overall application architecture.Optimize application performance, ensuring a smooth and efficient user experience.Troubleshoot and debug machine learning issues, identifying and implementing effective solutions.Participate actively in code reviews to uphold coding standards, enhance code quality, and promote best practices in machine learning development.Stay abreast of the latest trends, updates, and best practices in machine learning and software development.Provide mentorship and guidance to junior developers, sharing insights and expertise in machine learning engineering.

What skills should a Machine Learning Engineer possess?

The Machine Learning Engineers are instrumental in crafting, enhancing, and sustaining machine learning applications. Working collaboratively across teams, they ensure the effective integration of machine learning components, optimize application performance, and resolve intricate challenges.

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