• Table of Contents {toc}


  • look at Learning Strategy in concepts
  • about 10 mins of values

  • Each round below will also have questions about values
    • Snap Core Values
    • Kind - We listen from the heart, think empathetically, and help each other grow.
    • Smart - We think deeply, question conventions, and strive to never stop learning.
    • Creative - We challenge the status quo to make things with a sense of purpose.


  • 1h coding: Leetcode hard? easy? any particular data structures or algorithms, Graphs —> communicate while coding, trade offs, leetcode medium, Graphs/Trees questions
  • 1h ML fundamentals: Resume deep dive, expertise in recsys, large scale projects, cross functional, this type of work.. Skill fit. All fair game on resume. NLP experts will ask.
  • 1hr applied ML/ML design : building out a recommendation system, modeling component, ambiguous question, facebook friend recommendation. Ask clarifying questions, get a sense of design. Google draw
  • 1h system design: -> engineering side, system that the model sits in. Infrastructure focused, machine learning type question, infra side
    • Need to figure it out
  • 1h product-focused: -> competitive analysis, real time surface recommendation, corgi instantly, local model federated learning can update faster Yarun product focused, are you interested in whats going on with other companies. TikTok and reels familiar and interested in, cross collaborate, research per compnay
    • related papers and research analysis
  • 1h leadership / Q&A : Jun, people leadership, challenging conversations, cross functionally, promote others, priorritizing team (have values in mind, clarifying questions) concise communication, logical


  • First let’s start with Management frameworks

SWOT analysis

  • SWOT analysis is a strategic planning and strategic management technique used to help a person or organization identify Strengths, Weaknesses, Opportunities, and Threats related to business competition or project planning.
  • It is sometimes called situational assessment or situational analysis.
  • This framework helps managers assess an organization’s internal strengths and weaknesses and external opportunities and threats to develop strategies for growth and improvement.
  • Strengths: Strong brand reputation, skilled workforce, efficient supply chain.
  • Weaknesses: Outdated technology, high employee turnover, limited product diversity.
  • Opportunities: Emerging market trends, strategic partnerships, expanding customer base.
  • Threats: Intense competition, economic downturn, changing regulatory environment.


  • SMART is an acronym for Specific, Measurable, Achievable, Relevant, and Time-bound. This framework helps managers set clear and well-defined goals that are specific, measurable, attainable, relevant, and time-bound, increasing the likelihood of successful outcomes.
  • Specific: Increase sales by 10% in the next quarter.
  • Measurable: Achieve a customer satisfaction rating of 4.5 out of 5.
  • Achievable: Reduce production costs by 15% through process optimization.
  • Relevant: Launch a new product line to meet customer demands.
  • Time-bound: Complete the website redesign project within three months.

PDCA Cycle

  • PDCA stands for Plan, Do, Check, and Act. This iterative framework is used for continuous improvement and problem-solving. It involves planning a change, implementing it, checking the results, and acting on lessons learned to refine the process further.
  • Plan: Identify the problem, set improvement goals, and develop an action plan.
  • Do: Implement the plan on a small scale or pilot project.
  • Check: Measure and analyze the results to assess the effectiveness of the changes.
  • Act: Adjust the plan based on the lessons learned and scale up the improvements.

Agile Project

  • Management Agile is an iterative and flexible approach to project management. It emphasizes collaboration, adaptive planning, and continuous improvement. Agile frameworks like Scrum and Kanban enable teams to respond to change quickly and deliver value incrementally.
  • Scrum: Organize work into short iterations called sprints, with frequent feedback and adaptability.
  • Kanban: Visualize and manage workflow using a board, limit work in progress, and promote continuous delivery.

Six Sigma

  • Six Sigma is a data-driven approach to process improvement that aims to minimize defects and variability in processes. It uses statistical analysis and DMAIC (Define, Measure, Analyze, Improve, Control) methodology to identify and eliminate errors, reduce waste, and improve efficiency.
  • Define: Clearly define the problem, project goals, and customer requirements.
  • Measure: Gather data and measure the process performance.
  • Analyze: Analyze the data to identify the root causes of defects or inefficiencies.
  • Improve: Implement process improvements and verify their effectiveness.
  • Control: Establish control measures to sustain the improvements and monitor ongoing performance.

Balanced Scorecard

  • The Balanced Scorecard is a strategic management framework that measures organizational performance across multiple perspectives, including financial, customer, internal processes, and learning and growth. It provides a holistic view of the organization’s progress towards its strategic objectives.
  • Financial perspective: Increase profitability by reducing costs and increasing revenue.
  • Customer perspective: Improve customer satisfaction through exceptional service and product quality.
  • Internal process perspective: Streamline production processes to increase efficiency and reduce waste.
  • Learning and growth perspective: Enhance employee skills and knowledge through training and development.

Lean Management

  • Lean management focuses on eliminating waste and maximizing value for customers. It originated in manufacturing but has been applied to various industries. Lean principles aim to streamline processes, improve efficiency, reduce costs, and enhance customer satisfaction.
  • Identify and eliminate non-value-added activities or waste (e.g., overproduction, inventory, defects) to improve efficiency and reduce costs.

OKR (Objectives and Key Results)

  • OKR is a goal-setting framework widely used in the tech industry. It involves setting ambitious objectives and measurable key results to track progress. OKRs encourage transparency, alignment, and focus on outcomes rather than outputs.
  • Objective: Increase market share by 10%.
  • Key Result: Launch three new products within the next six months.


  • RICE is a management framework commonly used for prioritizing projects or initiatives. It helps decision-makers evaluate and rank ideas based on their potential impact, reach, confidence, and effort required. RICE stands for Reach, Impact, Confidence, and Effort. Here’s how each component is defined:
  • Reach: Reach measures the number of people or users who will be affected by the project or initiative. It helps assess the scale or magnitude of the impact. Reach can be quantified by estimating the total number of affected users, customers, or stakeholders.
  • Impact: Impact refers to the degree of benefit or value that the project or initiative is expected to deliver. It evaluates the potential positive outcomes and effects, such as revenue growth, cost savings, customer satisfaction improvement, or strategic alignment. Impact can be qualitative or quantitative, depending on the specific goals and metrics.
  • Confidence: Confidence represents the level of certainty or confidence that the project or initiative will achieve its intended impact. It takes into account factors like available data, market research, expert opinions, or historical performance. Confidence can be assessed on a scale (e.g., low, medium, high) or as a percentage.
  • Effort: Effort estimates the amount of time, resources, and effort required to implement the project or initiative. It considers factors like the complexity of the task, the number of people involved, and the availability of necessary resources. Effort is usually measured in terms of person-hours or person-days required.
  • To calculate the RICE score, each component (Reach, Impact, Confidence, Effort) is assigned a numerical value, typically on a scale of 1 to 10, with 10 being the highest. Then, the RICE score is calculated using the formula: RICE = (Reach × Impact × Confidence) / Effort.
  • The higher the RICE score, the higher the priority of the project or initiative. By using the RICE framework, organizations can prioritize their efforts and allocate resources effectively to projects with the greatest potential impact and value.

Change Management Models

  • Various change management models, such as Kotter’s 8-Step Process and ADKAR (Awareness, Desire, Knowledge, Ability, Reinforcement), provide a structured approach to managing organizational change. They help leaders navigate transitions, overcome resistance, and ensure successful implementation.
  • Kotter’s 8-Step Process: Create a sense of urgency, form a powerful coalition, communicate the vision, empower action, generate short-term wins, consolidate gains, anchor changes in the culture.
  • ADKAR: Create awareness of the need for change, build desire to support the change, provide knowledge and skills, enable employees to apply new skills, reinforce the change to make it stick.


  1. Explainability of Solution and ROI:
    • When implementing a solution or making a decision, it’s important for managers to ensure that the rationale and expected Return on Investment (ROI) are clearly communicated. Explainability refers to the ability to articulate why a particular solution or course of action was chosen and how it aligns with organizational goals. By providing a transparent and well-defined explanation of the solution and its expected ROI, managers can gain buy-in from stakeholders, foster understanding, and build confidence in the decision-making process.
  2. Prioritization:
    • Prioritization is the process of determining the order in which tasks, projects, or initiatives should be addressed based on their importance and urgency. Effective prioritization involves evaluating the relative value, impact, and alignment with strategic goals. Managers need to consider factors such as deadlines, resource availability, dependencies, and potential risks when setting priorities. Clear prioritization helps ensure that the most critical and high-value work is addressed first, optimizing productivity and outcomes.
  3. Absorb Leadership Requests:
    • As a manager, it is important to be able to absorb and handle requests from leadership effectively. This involves actively listening, understanding the requirements, and considering the impact on existing priorities and resources. By effectively absorbing leadership requests, managers can demonstrate their ability to balance and align these requests with the broader goals and objectives of the team or organization. It also helps in managing expectations and effectively communicating any constraints or trade-offs that may arise.
  4. Coaching Metrics:
    • Coaching metrics involve tracking and evaluating the progress and development of individuals or teams. These metrics provide quantitative or qualitative insights into the effectiveness of coaching efforts. Examples of coaching metrics include employee engagement scores, performance improvement indicators, skill development assessments, or feedback from peers and stakeholders. By monitoring coaching metrics, managers can identify areas for improvement, measure the impact of coaching interventions, and tailor their coaching approaches to support employee growth and performance.
  5. High/Low Performers:
    • Managing high and low performers is an important aspect of effective management. High performers are individuals who consistently exceed expectations, demonstrate exceptional skills, and contribute significantly to the team’s success. Managers should recognize and reward high performers, provide opportunities for growth and advancement, and leverage their expertise to drive team performance. On the other hand, low performers may require additional support, coaching, or performance improvement plans. Managers need to address performance issues promptly, provide constructive feedback, and offer development opportunities to help low performers improve or make necessary changes to the team composition.
  6. Escalations: Ensuring Awareness and Alignment:
    • Escalations occur when a situation or issue requires immediate attention from higher levels of management or stakeholders. Effective management of escalations involves ensuring that both parties are aware and aligned regarding the situation, its urgency, and the desired outcome. Managers should establish clear escalation channels and protocols, communicate the escalation process to the team, and ensure that relevant stakeholders are kept informed. By managing escalations effectively, managers can address critical issues promptly, maintain transparency, and facilitate timely decision-making.
  7. Joint Incentives:
    • Joint incentives refer to aligning incentives and rewards across teams or departments to encourage collaboration and shared goals. By creating joint incentives, managers foster a sense of collective ownership and encourage cross-functional collaboration. This approach promotes a collaborative culture, breaks down silos, and motivates teams to work together towards common objectives. Joint incentives can include shared targets, recognition programs, or performance bonuses tied to overall organizational success.
  8. Trust and Company Goals:
    • Trust is a vital element in effective management. Managers need to build trust among their team members and stakeholders by fostering open communication, demonstrating integrity, and delivering on commitments. Trust enables collaboration, empowers employees

Debugging ML Models

  • Check your data: Start by examining your data to ensure its quality and correctness. Look for missing values, outliers, or inconsistent data formats. Data preprocessing steps like data cleaning, normalization, and feature engineering can also introduce errors, so validate those steps.
  • Review your model architecture: Verify that your model architecture is appropriate for your problem. Check if the model is too complex, leading to overfitting, or too simple, resulting in underfitting. You can try simpler models, regularization techniques, or adjusting hyperparameters to improve performance.
  • Inspect your model’s predictions: Examine the predictions your model is making to identify patterns and potential issues. Compare predicted outputs with the ground truth values. Look for systematic errors, such as consistently overpredicting or underpredicting certain classes or regions of the input space
  • Evaluate performance metrics: Assess various performance metrics, such as accuracy, precision, recall, or F1 score, depending on your problem type. These metrics can give you insights into the strengths and weaknesses of your model. Consider using validation and test datasets to assess performance and avoid overfitting.
  • Visualize model behavior: Visualizations can help you understand what your model is learning and how it’s making predictions. For example, you can visualize feature importances, decision boundaries, or activation maps in convolutional neural networks. Visualizations can reveal issues like data leakage, biased predictions, or mislabeled data.
  • Isolate and reproduce issues: If you’re encountering specific issues or errors, try to isolate and reproduce them. Simplify your inputs or modify your code to create a minimal example that highlights the problem. This can help you understand the root cause and develop potential solutions.
  • Debug incrementally: Rather than trying to tackle all potential issues at once, narrow down the possibilities. Gradually debug and validate each component of your machine learning pipeline, such as data preprocessing, model architecture, loss function, or optimization algorithm.
  • Check assumptions: Make sure you’re not violating any assumptions underlying your machine learning algorithm. For instance, linear models may assume linearity, and tree-based models assume feature independence. Violating these assumptions can lead to poor performance.
  • Cross-validation and ensemble methods: Cross-validation can provide a more robust estimate of your model’s performance. It helps identify issues related to data distribution, overfitting, or generalization. Ensemble methods, such as bagging or boosting, can also help improve model performance and reduce overfitting.
  • Follow strategies here