Preparing for the Tessian interview process requires a shift in mindset from standard data science interviews. You need to be ready to demonstrate not just theoretical knowledge, but also the ability to write production-ready code and architect systems from scratch.

Technical Proficiency – You will be evaluated heavily on your ability to write clean, efficient Python code. Unlike some data science roles where scripting is enough, Tessian expects strong algorithmic fundamentals. You must be comfortable implementing logic that goes beyond library imports, such as parsing expressions or manipulating complex data structures.

Problem Structuring & Product Sense – Tessian places a high value on how you approach ambiguous problems. You will likely face case studies related to email security (e.g., "How would you detect if an email is fake?"). Interviewers are looking for a structured approach: identifying data sources, recognizing data quality issues (dirty data), designing the high-level flow, and iterating on edge cases.

ML System Design – For mid-to-senior roles, you must demonstrate an understanding of the end-to-end ML lifecycle. This includes discussing architecture, how to serve models in real-time, and how to handle feedback loops. You should be prepared to sketch out how a solution fits into a larger platform, rather than just describing a specific algorithm.

Communication & Collaboration – You will often be interviewed by the people you will work with daily. They assess whether you can explain complex technical nuances to cross-functional stakeholders. Being able to articulate why a specific model is the right choice—and acknowledging its limitations—is just as important as the choice itself.

The interview process at Tessian is generally regarded as rigorous and structured, with a difficulty level often described as "Hard" or "Very Hard." While historical data suggests some variability in the candidate experience regarding feedback timelines, recent reports indicate a well-organized process designed to test relevant skills deeply. You should expect a multi-stage funnel that moves from automated screening to intense interactive problem-solving.

Typically, the process begins with a recruiter screen followed by a technical assessment. This assessment is often a HackerRank-style coding challenge focused on Python and algorithms. If you pass this stage, you will move to a series of virtual onsite rounds. These rounds are distinct and cover specific competencies: a deep-dive technical interview (often involving a case study), a system design or architecture discussion, and behavioral interviews to assess culture fit and collaboration style.

The philosophy behind this process is to simulate the day-to-day work at Tessian. The coding questions are not just abstract puzzles but often relate to parsing or data manipulation tasks you might encounter in backend systems. Similarly, the case studies are drawn directly from the challenges of detecting email threats. You should be prepared for a process that tests your stamina and your ability to think on your feet under pressure.

This timeline illustrates the typical progression from application to final decision. Note that the "Technical Assessment" is a critical gatekeeper; rigorous preparation for this automated stage is essential to unlocking the face-to-face rounds. The final stage is usually a cluster of interviews, so ensure you manage your energy levels for a half-day of engagement.

Candidates are evaluated across three primary pillars: Algorithmic Coding, ML Case Study/Design, and Behavioral Fit. Understanding the specifics of these pillars is key to success.

Algorithmic Coding & Python

This is the most strictly evaluated technical filter. You are expected to be proficient in Python and capable of solving algorithmic problems under time constraints.

• Why it matters: Tessian’s models must run efficiently at scale. Data Scientists are expected to contribute production-quality code.
• Evaluation: Focus is on correctness, efficiency (Big O notation), and code cleanliness.
• Strong performance: You solve the problem, handle edge cases, and write readable code that follows PEP 8 standards.

Be ready to go over:

• String Manipulation & Parsing: Evaluating mathematical expressions or parsing text strings.
• Data Structures: Using stacks, queues, and dictionaries effectively.
• Time Complexity: Explaining the efficiency of your solution.

Example questions or scenarios:

• "Write a program to evaluate prefix or infix mathematical expressions." (This is a frequently reported question type).
• "Given a stream of characters, identify specific patterns or validity."

ML Case Study & Problem Solving

This is often the core of the onsite interview. You will be presented with an open-ended problem relevant to Tessian’s domain.

• Why it matters: It tests your ability to translate a vague business problem (e.g., "stop fraud") into a concrete technical solution.
• Evaluation: Interviewers look for a structured approach: Data -> Feature Engineering -> Model Selection -> Metrics -> Deployment.
• Strong performance: You proactively identify issues with "dirty data" before jumping to modeling. You can draw a high-level flow diagram and discuss trade-offs.

Be ready to go over:

• Anomaly Detection: Approaches for identifying outliers in user behavior.
• NLP Techniques: Tokenization, embeddings, and classification for text.
• Data Quality: Identifying biases or noise in a provided dataset.

Example questions or scenarios:

• "How would you build a system to detect brand impersonation in emails?"
• "Here is a sample dataset of email headers and bodies. What issues do you see with this data?"
• "Design a flow to flag emails that look like they come from a CEO but don't."

System Design & Architecture

For more senior roles, this section tests your ability to think big picture.

• Why it matters: Models do not exist in a vacuum. They must integrate with the broader Tessian platform.
• Evaluation: Ability to design scalable, reliable systems.
• Strong performance: You discuss latency, throughput, database choices, and how to monitor model performance in production.

Be ready to go over:

• ML Platform Design: How to automate training and deployment pipelines.
• Real-time vs. Batch: When to score emails (synchronously vs. asynchronously).

As a Data Scientist at Tessian, your day-to-day work revolves around the intersection of data exploration and software engineering. You are responsible for the end-to-end lifecycle of machine learning models. This starts with exploratory data analysis on vast amounts of email and communication data to understand attack patterns. You will spend significant time cleaning and curating these datasets, as real-world email data is notoriously "dirty" and unstructured.

Once the data is prepared, you will design and train models—often utilizing NLP and graph theory—to detect specific threats like spear-phishing or accidental data exfiltration. However, the responsibility does not end at model training. You will collaborate closely with platform engineers to deploy these models into production environments where they must operate with low latency. You will also monitor these models to ensure they adapt to shifting attack vectors, requiring a continuous loop of feedback and improvement.

To be competitive for this role, you need a strong foundation in both the theory of machine learning and the practice of software development.

• Must-have skills:

  • Python Mastery: You must be fluent in Python, including standard libraries and data structures. This is non-negotiable for passing the initial screens.
  • Applied ML Experience: Experience building models for NLP, classification, or anomaly detection problems.
  • Production Mindset: Experience or a strong willingness to write code that is deployed to production, not just Jupyter notebooks.
  • Communication: Ability to explain complex probabilistic outcomes to non-technical stakeholders.

• Nice-to-have skills:

  • Cybersecurity Domain Knowledge: Understanding of phishing, social engineering, or email protocols (SMTP, headers).
  • Graph Data Science: Experience working with network graphs (e.g., analyzing relationships between employees).
  • Cloud Platforms: Familiarity with AWS or similar cloud environments for deploying ML services.