Fractal Analytics is a global leader in artificial intelligence and analytics, partnering with Fortune 500 companies to drive data-driven decisions. With a mission to empower imagination with intelligence, Fractal fosters a culture where innovation and diversity are key assets. Recognized as a "Great Place to Work" by The Economic Times in partnership with the Great Place to Work® Institute, Fractal is known for its innovative approach to AI solutions.

The Data Scientist position at Fractal involves implementing advanced statistical and machine learning techniques to solve complex business problems. Candidates will engage in various facets of the data science lifecycle, from data gathering to model deployment. If you are a problem solver with a strong grasp of ML algorithms and a proactive mindset, this role offers an excellent opportunity to grow and make impactful contributions.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Fractal Analytics as a data scientist. Whether you were contacted by a Fractal recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Fractal Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Fractal Analytics hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Online Assessment

Successfully navigating the recruiter round will present you with an invitation for the online assessment. This technical test is conducted through virtual means, often using platforms like HackerRank or DoSelect. The test usually consists of a mix of multiple-choice questions (MCQs) and programming challenges.

• Coding Questions: Typical assignments might include real-world data problems such as Wine Quality Prediction or Room Occupancy Based on Environmental Factors. Conclusively solving these problems might require implementing models using algorithms like KNN or Random Forest.
• MCQs: The questions typically assess your understanding of advanced machine learning and deep learning concepts, covering areas such as Bayesian optimization, boosting, statistics, and image processing.

Technical Rounds

1. Technical Interview with a Senior Data Scientist:
2. Duration: 60 minutes.

3. Focus Areas: In-depth discussion revolving around machine learning concepts, NLP techniques, and math and statistics behind the techniques. Expect scenario-based questions and live coding activities where you'll delve into specific approaches cited on your resume.

4. Interview with VP of AI:

5. Duration: 30 minutes.
6. Focus Areas: Discussion regarding your past projects, your skillsets, and alignment with Fractal’s objectives. You may also receive questions aimed at understanding your interests and long-term goals.

HR Round

In the final HR round, the focus will shift towards behavioral questions centered on cultural fitment, long-term goals, reasons for job change, and salary expectations. Notably, Fractal’s HR representatives usually handle this round with a fixed agenda, leaving little room for negotiation.

Quick Tips For Fractal Analytics Data Scientist Interviews

• Brush Up On Basics: Fractal places heavy emphasis on fundamental machine learning concepts. Ensure you have a solid grasp of ML algorithms, NLP techniques, and statistical fundamentals.
• Understand Real-World Applications: Be prepared to discuss your previous projects in detail and how your contributions made a difference. Explain the business implications of your projects and how they align with Fractal’s goals.
• Efficient Problem-Solving: Time management is crucial during coding assessments. Practice efficiently solving coding problems and understand the trade-offs between different machine learning models to ensure you meet the criteria within the given time frame.

Typically, interviews at Fractal Analytics vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• Select the top 3 departments with at least ten employees and rank them by the percentage of employees making over 100K. Given employees and departments tables, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

• What kind of model did the co-worker develop for loan approval? Your co-worker developed a model that takes customer inputs and returns a decision on whether a loan should be given or not. Identify the type of model used.

• How would you compare two credit risk models for predicting loan defaults? Given that personal loans are monthly installments, how would you measure the difference between two credit risk models within a specific timeframe?

• What metrics would you track to measure the success of a new credit risk model? Identify the key metrics you would track to evaluate the performance and success of a new credit risk model.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms and explain the scenarios where you would use a bagging algorithm instead of a boosting algorithm. Provide examples of the tradeoffs between the two.

• How would you detect firearm listings on a marketplace? Design a system to automatically detect if a listing on your website's marketplace is selling a gun, considering that selling firearms is prohibited by your website's Terms of Service Agreement and the laws of your country.

• How would you design a model to map legal first names to likely nicknames? As a data scientist at Facebook, you need to generate a machine learning model that can map the legal first name of a person to likely nicknames. Describe your approach to designing this model.

• How would you tackle multicollinearity in multiple linear regression? Explain the methods you would use to address multicollinearity when performing multiple linear regression.

• How would you set up an A/B test for button color and position changes? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you set up this test?

• How would you forecast Facebook's revenue for the next year? You work on the revenue forecasting team at a company like Facebook. An executive asks you to forecast the company's revenue for the coming year. How would you approach this task?

• How would you determine if a redesigned email campaign led to an increase in conversion rates? An E-commerce store's new marketing manager redesigned the new-user email journey, and the conversion rate increased from 40% to 43%. However, the rate was 45% a few months prior. How would you investigate if the redesign caused the increase?

• How would you ensure data quality across different ETL platforms for PayPal's market research? PayPal partnered with a local survey platform for market research in Southern Africa. The data includes pre-quantified and text data in different languages. How would you ensure data quality across ETL pipelines connecting PayPal’s data marts with the survey platform’s data warehouses?

• How would you conduct an experiment to test Uber's new ETA range feature? A PM at Uber is considering a new feature that displays an ETA range (e.g., 3-7 minutes) instead of a direct estimate (e.g., 5 minutes). How would you conduct this experiment and determine if the results are significant?

• What is the mean and variance of the distribution of (2X - Y) given (X) and (Y) are independent normal variables? Given (X \sim N(3, 2^2)) and (Y \sim N(1, 2^2)), calculate the mean and variance of the distribution of (2X - Y).

What is the average salary for a Data Scientist at Fractal Analytics?

Q: What does the interview process at Fractal Analytics look like for a Data Scientist position? The interview process usually comprises four rounds:

1. An online assessment covering Python and SQL.
2. A technical round focusing on Machine Learning concepts, particularly those mentioned in your resume. Expect scenario-based questions and discussions on NLP techniques, as well as the underlying math and statistics.
3. A round with a senior executive, often the VP of AI, to discuss your projects, skills, and how they align with Fractal’s objectives.
4. An HR round to address any final queries and confirm offer details.

Q: What types of questions are asked in the online assessment test? The online assessment consists of a mix of multiple-choice questions and coding problems, emphasizing advanced Machine Learning topics. You may encounter ML questions around image processing, ReLU, and implementing algorithms such as k-NN and Random Forest. One of the popular questions revolves around the Wine Quality Prediction.

Q: What technical skills should I have to apply for the Data Scientist role at Fractal Analytics? You should have strong proficiency in Python, especially libraries such as Pandas, Scikit-learn, TensorFlow, or PyTorch. Skills in SQL for data manipulation and querying are essential. A solid understanding of Machine Learning concepts, including supervised and unsupervised learning, neural networks, and NLP, is crucial. Familiarity with statistical analysis and data preprocessing is also expected.

Q: Can you describe the company culture at Fractal Analytics? Fractal Analytics promotes a culture of creativity, collaboration, and innovation. They place a significant focus on individual choices and diversity, aiming to empower employees' imagination with intelligence. The working environment is supportive yet challenging, encouraging employees to constantly adapt and learn.

Q: How should I prepare for an interview at Fractal Analytics? Research the company extensively and review their latest projects and technologies. Brush up on your Python, SQL, and Machine Learning skills by practicing problems on Interview Query. Be prepared to discuss your past projects in detail, focusing on the challenges faced and solutions implemented. Lastly, ensure you can explain complex technical concepts in simple terms, which is often asked in VP or senior-level discussions.

The interview process at Fractal Analytics for a Data Scientist position is methodically structured, encompassing a variety of assessments and discussions that ensure a comprehensive evaluation of a candidate's technical and problem-solving abilities. The experience typically starts with an online assessment focusing on Python and SQL, progresses through technical rounds that delve into machine learning concepts, project-based discussions, and advanced ML techniques, and culminates with HR discussions.

Overall, candidates have reported a generally positive experience with an average difficulty level, although some encountered challenges with the interview platform and response times. If you want more insights about the company, check out our main Fractal Analytics Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Fractal's interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Fractal Analytics machine learning engineer interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!