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%

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The Andela Standard: Network Entrance Assessments

Successful candidates exhibit proficiency in data science tools, demonstrate a deep understanding of data architecture, and showcase problem-solving abilities in dynamic analytical environments.

1. Qualifications

Qualification criteria:

  • A minimum of 4 years of professional experience in a Data Analysis 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 data ecossystem with a solid understanding of data science principles.
  • Hands-on experience with data-related technologies, including databases, querying languages, and statistical analysis tools.
  • Strong problem-solving skills, attention to detail, and a results-oriented mindset in the realm of data analysis.
  • A commitment to continuous learning and adapting to evolving data technologies.

2. Skill Validation

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

Probability and Statistics Fundamentals

This challenge tests familiarity with Probability and Statistics applied to Data Science. It should be suitable for mid-to-senior candidates. Most of the questions are derived from Data Science certifications and some inspired by frequent Data Science interview questions.

Data Science Fundamentals

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

SQL

This challenge tests candidate knowledge of SQL and associated best practices. 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 expertise in Data Science through a real-time coding challenge.During the assessment, technologists will tackle a hands-on problem tailored to the core principles of Data Science.

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 step in to assess the overall efficiency of the candidate's solution.

Assessment Outline:

Data Science Coding Challenge:

  • 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 to intermediate SQL problem that asks the technologist to query sales data. It tests the technologists’ ability to write subqueries using the WITH keyword in SQL.

Build Your Data Science Dream Team

Avinash N.
Data Scientist

Experience:

9 years

Availability:

Full-time
Avinash is a seasoned Data Scientist, Machine Learning Engineer, Gen Ai Scientist - with almost a decade of proven expertise in LLM, Langchain, Generative Ai, data analysis, predictive modeling, and business intelligence. His proficiency extends to cutting-edge technologies such as Generative Ai, LLM, Deep Learning, neural networks, natural language processing (NLP), TensorFlow, and Keras. He excels in Python, Snowflake, AWS, SQL, Machine Learning, and statistical modeling, Project Management, decision-making through data-driven strategies
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Expert in
Omotayo I.
Data Analyst

Experience:

10+ years

Availability:

Full-time
Seasoned Data professional with 8+ years' expertise in statistical modeling, data automation, and impactful dashboard creation. Skilled in Python, R, SQL, and BigQuery, I specialize in microfinance, leveraging A/B testing, quantitative research, and statistical computing to address complex challenges. Proven success in developing credit scoring models, client churn predictions, and accurate forecasting solutions.
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Expert in
Anurag P.
Data Scientist

Experience:

10+ years

Availability:

Full-time
Anurag is an inquisitive Data Scientist with 8+ years of professional experience uncovering complex problems and implementing innovative solutions through advanced analytics and Machine Learning. He is proficient in Python, R, BigQuery, SQL, Looker, Keras, TensorFlow, and Tableau. Anurag possesses a deep understanding of time series forecasting, random forest, Lasso regression, and XGBoost. 
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Expert in
Alejandro R.
Data Engineer

Experience:

4 years

Availability:

Full-time
Alejandro is a Data & DataOps Engineer with 4+ years of experience in analytics, data, and business applications (pricing, marketing, product, and operations). He has widely contributed to CI/CD pipelines and cloud services to deliver highly maintainable and scalable data products.
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Frequently Asked Questions

Where can I hire Data Scientists?

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

How are Andela Data Engineers different?

Andela Data Scientists 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 Data Science 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 Data Scientists through Andela?

Andela’s streamlined hiring process ensures you can access top Data Scientists 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 Data Science team rapidly, without compromising on quality.

What are the minimum requirements of all Andela Data Scientists?

All Andela Data Scientists must meet the following requirements to be eligible to apply to the network:

  • A minimum of 4 years of professional experience in a Data Analysis 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 data ecossystem with a solid understanding of data science principles.
  • Hands-on experience with data-related technologies, including databases, querying languages, and statistical analysis tools.
  • Strong problem-solving skills, attention to detail, and a results-oriented mindset in the realm of data analysis.
  • A commitment to continuous learning and adapting to evolving data technologies.

What are the typical responsibilities of a Data Scientist?

  • Analyze, model, and maintain scalable and insightful data science solutions using advanced techniques and technologies.
  • Collaborate with cross-functional teams to ensure seamless integration of data science components into the overall analytical architecture.
  • Optimize analytical performance, ensuring accurate and efficient extraction of insights from complex datasets.
  • Troubleshoot and resolve data-related issues, identifying and implementing effective solutions for seamless data analysis.
  • Participate actively in code reviews to uphold coding standards, improve code quality, and advocate for best practices in data science.
  • Stay abreast of the latest trends, updates, and best practices in data science development.
  • Provide mentorship and guidance to junior data scientists, sharing insights and expertise in data science principles and practices.

What skills should a Data Scientists possess?

Data Scientists play a key role in crafting, enhancing, and extracting insights from complex datasets. Working collaboratively across teams, they ensure effective utilization of data science methodologies, optimize analytical performance, and address intricate challenges in data interpretation. Successful Data Scientists should exhibit proficiency in data science tools, demonstrate a deep understanding of data architecture, and showcase problem-solving abilities in dynamic analytical environments.

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