Core Machine Learning & Advanced Modeling

This evaluation area focuses on your theoretical understanding of machine learning and your ability to apply these concepts to practical, real-world problems.

Interviewers are not looking for rote memorization of formulas. They want to see that you understand the underlying mechanics of algorithms and can make pragmatic decisions when building models in a production environment.

Be ready to go over:

• Model Selection & Tuning – Knowing when to use linear models, tree-based ensembles, or neural networks based on data constraints.
• Evaluation Metrics – Choosing the right metric (Precision-Recall, ROC-AUC, Mean Absolute Percentage Error) that aligns with business objectives.
• Modern Architectures – Understanding the fundamentals of transformer models, embeddings, and practical LLM implementation.
• Advanced concepts (less common) – Bayesian optimization, causal inference, and multi-task learning frameworks.

Example questions:

• "How would you design a recommendation system for a platform with sparse user-interaction data?"
• "Explain how you would implement and evaluate a retrieval-augmented generation (RAG) system for internal document search."

SQL, Analytics & Business Logic

This area tests your hands-on data manipulation skills and your ability to translate raw data into structured business logic.

You will face live coding exercises and case studies that require you to write clean, optimized code while explaining your thought process out loud.

Be ready to go over:

• Advanced SQL – Window functions, complex joins, common table expressions (CTEs), and query optimization techniques.
• Data Wrangling – Efficiently cleaning and aggregating data using Python (Pandas/NumPy) or R.
• Business Translation – Creating logical frameworks to solve ambiguous business problems and defining clear, measurable KPIs.

Example questions:

• "Write a SQL query to calculate the retention rate of users cohort by cohort on a monthly basis."
• "How would you design an algorithm to automatically flag anomalous transaction behavior in real-time?"

As a Data Scientist at Syntes, your day-to-day work will be highly dynamic and intellectually stimulating. You will be responsible for leading data-driven initiatives that directly impact the company's core products and operational workflows.

You will design, develop, and deploy predictive models and analytical pipelines to solve complex business challenges. This involves everything from raw data extraction and preprocessing to model training, evaluation, and deployment. You will work closely with engineering teams to ensure your models are scalable, robust, and integrated seamlessly into production environments.

Collaboration is a core component of this role. You will act as a trusted technical advisor to business stakeholders, particularly in Finance and Operations. You will translate complex statistical findings into clear, actionable recommendations that guide strategic decision-making. Additionally, you will contribute to the team's shared codebase, participate in peer code reviews, and help mentor junior team members, fostering a culture of continuous learning and technical excellence.

To be competitive for the Data Scientist position at Syntes, you must possess a strong foundation in quantitative disciplines and a proven track record of delivering impactful data products.

Technical and Experience Requirements

• Education – A Master's or Ph.D. in a highly quantitative field such as Computer Science, Statistics, Mathematics, Physics, or a related discipline is highly preferred.
• Experience – Typically 3+ years of professional experience working as a Data Scientist or Machine Learning Engineer, with a track record of deploying models to production.
• Programming Languages – Advanced proficiency in Python or R, and expert-level SQL skills are mandatory.
• Machine Learning Frameworks – Hands-on experience with modern ML libraries such as PyTorch, TensorFlow, Scikit-Learn, and XGBoost.
• Modern NLP & LLMs – Experience working with transformer models, prompt engineering, and fine-tuning LLMs is highly advantageous.

Preferred Qualifications

• Must-have skills – Strong statistical modeling foundations (hypothesis testing, regression, experimental design), excellent presentation skills, and the ability to write production-grade code.
• Nice-to-have skills – Experience working with cloud platforms (AWS, GCP, or Azure), familiarity with big data technologies (Spark, Hadoop), and a background in financial modeling or econometrics.

Q: How difficult is the Data Scientist interview at Syntes? A: The interview process is generally rated as average to difficult. It is highly technical and requires a deep, first-principles understanding of your past work and core machine learning concepts. It is not a process you can pass through memorization; practical application and clear communication are key.

Q: What is the typical timeline from the first screen to an offer? A: Because the process involves multiple rounds and various stakeholders (including senior leadership and cross-functional partners), the timeline typically spans 4 to 8 weeks. Delays can occasionally occur during holiday seasons or when coordinating large panel presentations.

Q: How much coding should I expect in the interview? A: You should expect at least one dedicated live coding round focusing on SQL and algorithmic problem-solving in Python or R. Additionally, you must be prepared to discuss the software engineering aspects of your past projects, including code structure and deployment.

Q: Is there an emphasis on product and business sense? A: Yes. Syntes values data scientists who can connect their models to business outcomes. You will face questions designed to test how you apply analytics to solve business logic problems and how you measure the success of your models in financial or operational terms.

To maximize your chances of success, keep these practical, insider tips in mind as you prepare:

• Master your own CV: Do not list any technology, algorithm, or metric on your resume unless you can explain its inner workings and defend why you used it. Interviewers will grill you on the details.
• Structure your presentation clearly: Treat your project presentation like a story. Clearly define the problem, explain your technical approach and the trade-offs you made, show the results, and candidly discuss the limitations.
• Communicate your thought process: During live coding and SQL rounds, do not code in silence. Talk through your logic, explain the trade-offs of your approach, and verify your assumptions with the interviewer.
• Prepare for the situational assessments: Take the initial personality and scientific quizzes seriously. They are designed to evaluate your moral reasoning, situational judgment, and how you approach scientific problem-solving.

• Ask insightful questions: At the end of each round, use your time to ask thoughtful questions about the team's technical stack, their current challenges, and how they measure the impact of data science within the organization.

The Data Scientist position at Syntes is an exceptional opportunity for quantitative professionals who want to work on complex, high-impact problems at a global scale. The role offers a unique combination of technical challenge, cross-functional collaboration, and the ability to see your models directly influence business strategies and product directions.

As you begin your preparation, focus on mastering the details of your past projects, refining your core machine learning and statistical knowledge, and practicing your SQL and presentation skills. A structured, thorough approach to your preparation will make a significant difference in your performance and confidence throughout the interview loop.

To gain deeper insights, explore detailed salary data, and review more real-world interview experiences from candidates who have gone through the process, make sure to utilize the resources available on Dataford. Good luck with your preparation—you have the tools and the knowledge to succeed.

The compensation data above reflects the competitive salary ranges offered for the Data Scientist role at Syntes. When evaluating this data, consider that total compensation often includes base salary, performance bonuses, and equity components. Use this information to align your expectations and confidently navigate your compensation discussions during the final stages of the hiring process.