Everything You Need to Know about Data Science Consulting (Gleb Drobkov) - KNN Ep.23

Ken's Nearest Neighbors Podcast

25 Nov 202039:18

EducationalLearning

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TLDRIn this insightful interview, Glee, a senior data scientist at BCG's Gamma advanced analytics practice, shares his unique journey into data science from a background in economics and his experiences working in consulting. He discusses the differences between data science in consulting firms and traditional industries, the interview process for data science roles, and the importance of client readiness. Glee also highlights the value of personal projects in demonstrating one's abilities and the potential for data science to contribute to social good through NGO collaborations.

Takeaways

🎓 The guest, Glebe, transitioned from studying economics and statistics to becoming a senior data scientist, highlighting that a non-traditional background can successfully lead to a career in data science.
💡 Data scientists in consulting firms like BCG Gamma focus on using data and machine learning to help brands and retailers connect with consumers in more personalized ways.
🚀 The interview process for data science roles in consulting firms emphasizes a generalist mindset and the ability to apply data insights across various industries, rather than deep subject matter expertise.
🌟 Client readiness is crucial in consulting roles, where data scientists often interact with high-level executives and must be able to communicate complex ideas clearly and effectively.
🔄 The project structure in consulting data science involves shorter, more dynamic projects compared to traditional industry roles, with a focus on immediate impact and delivering deployable solutions.
🛠️ Data scientists in consulting are involved in the full lifecycle of projects, from initial SQL queries to collaborating with engineering teams for data pipeline development.
📊 The deliverables in consulting data science projects typically involve a complete data system and process, which is piloted and then handed over to the client's team for maintenance and scaling.
🔧 The transition from project development to client ownership is facilitated by treating clients as owners from the start and ensuring that the code is written in an agile, maintainable way.
🌐 Personalization and targeted marketing are key areas where data science is making a significant impact, with the potential to transform retail and customer engagement.
🌍 Data science has a substantial role in social good, with opportunities for professionals to contribute to NGOs and non-profits by analyzing data for better decision-making and resource allocation.
📈 The demand for data scientists continues to grow, and the field is accessible to those with passion and dedication, regardless of their initial academic background.

Q & A

What does the consultant's perspective on taking pride in their work entail?
-For consultants, taking pride in their work means focusing on ensuring that the clients they work for look really good and achieve success, rather than seeking personal credit.
How did Glebe transition from studying economics to becoming a data scientist?
-Glebe's interest in data analysis and quantitative methods, sparked by his minor in statistics during his economics studies, led him to management and technology consulting. He then morphed into a data scientist by learning to code and taking on personal projects, such as stock market analysis, to build up his competencies in programming languages.
What are some key differences between a data science role in a consulting firm and a traditional data science role in an industry-specific company?
-In a consulting firm, data scientists often work on a variety of projects across different industries, focusing on delivering results and immediate impact. In contrast, industry-specific roles may involve deeper subject matter expertise and longer project durations, with a focus on optimizing existing processes and systems.
How does the interview process for a data science role in a consulting firm differ from that of an industry role?
-Consulting firm interviews are more likely to focus on a generalist's ability to think through problems using data inputs to drive solutions, whereas industry interviews might require subject matter expertise specific to the company's domain.
What is the significance of client readiness in a consulting role?
-Client readiness is crucial in consulting as it involves the ability to interact effectively with clients, understand their needs, and deliver solutions that can be adopted and owned by the client's team for long-term success.
How does Glebe describe the typical project structure and duration in consulting versus a traditional industry role?
-In consulting, projects are shorter, often ranging from three to six months, with a focus on delivering results and immediate impact. In contrast, traditional industry roles may involve longer projects, such as six to 12 months, with a more risk-averse and slower-moving approach.
What are some deliverables that consulting data scientists might produce for their clients?
-Deliverables can include data systems, processes, and algorithms that are piloted in conjunction with the client's team. These could be deployed into the client's cloud environment and maintained with their IT team for ongoing use.
How does Glebe describe the team structure and organization within a consulting firm like BCG?
-The team structure is lean and project-based, with data scientists working closely with strategy and finance teams. Each project, or 'module', has specific goals and timelines, and team members may include data engineers and machine learning specialists.
What is the process for scoping and planning a data science project in a consulting environment?
-Consultants work with clients to understand the value opportunity, craft an organizational change management plan, and develop algorithms with input from all relevant stakeholders. The process involves setting clear goals, timelines, and deliverables for each project module.
What are some types of problems that consulting data scientists typically address?
-Consulting data scientists address a range of problems, from personalization and targeted marketing to projects with social impact, such as improving healthcare decisions in developing communities.
What advice does Glebe have for aspiring data scientists and those looking to transition into the field?
-Glebe advises aspiring data scientists to stay focused, continue learning, and not be afraid to make the leap into the field. He emphasizes the importance of passion and the availability of resources to learn, and encourages reaching out for help and guidance.

Outlines

00:00

🤝 Career Paths in Data Science and Consulting

The discussion begins with the host and guest, Glee, sharing their high school experiences and how they both ended up in data science. The guest, Glebe, talks about his non-traditional path into data science, starting with economics and statistics, and then moving into consulting at Capgemini. He shares how he transitioned into data science over time, learning to code and working on personal projects to build his skills.

05:00

📚 Learning Data Science and Technical Skills

Glee and the host delve into the process of learning technical aspects of data science. Glee explains that he started by taking online courses and working on personal projects, such as stock market analysis, to build his coding skills. The conversation highlights the importance of passion and self-driven learning in mastering data science.

10:01

💼 Data Science Roles and Interview Processes

The host and Glee discuss the differences between traditional data science roles and consulting roles, focusing on the interview process. Glee explains that consulting firm interviews are more about general problem-solving skills, while industry roles might require subject matter expertise. They also touch on the importance of client readiness and how consulting firms assess this quality in candidates.

15:01

🔄 Project Structure and Team Dynamics in Consulting

Glee shares insights into the project structure and team dynamics in consulting, contrasting it with industry roles. He describes how consulting projects are shorter and more varied, requiring data scientists to quickly establish processes and deliver results. The conversation also covers the collaborative nature of consulting work and the importance of making an immediate impact.

20:03

🚀 Deliverables and Client Engagement in Consulting

The discussion shifts to the types of deliverables in consulting projects, ranging from dashboards to APIs. Glee emphasizes the importance of client engagement and ensuring that the solutions provided are sustainable and maintainable. He also talks about the process of handing over projects to client teams and the role of consultants in supporting this transition.

25:04

🌐 Team Structure and Project Modules in BCG

Glee provides an overview of the team structure and project modules at BCG, explaining the concept of modules and how they function within the consulting framework. He discusses the roles of data scientists and the collaboration between different teams, including strategy and implementation sides.

30:05

📈 Scoping and Planning Data Science Projects

The conversation focuses on the process of scoping and planning data science projects, both personal and professional. Glee shares his approach to project planning, emphasizing the importance of setting realistic timelines and milestones. He also discusses the balance between exploratory data analysis and hypothesis testing in the project lifecycle.

35:06

🌟 Personalization and Social Impact in Data Science

Glee talks about the growing importance of personalization in data science, particularly in marketing and consumer engagement. He also highlights the potential for data science to have a significant social impact, especially in non-profit and healthcare sectors. The discussion touches on the role of data scientists in driving better decision-making and resource allocation in developing economies.

🤗 Encouragement and Final Thoughts from a Data Science Professional

In the concluding segment, Glee offers words of encouragement to aspiring data scientists, emphasizing that the field is accessible to those with passion and dedication. He shares his own journey from studying economics to becoming a data scientist and encourages listeners to stay focused and reach out for guidance.

Mindmap

Keywords

💡Data Science

Data Science refers to the field of study concerned with the processes and systems for extracting knowledge or insights from data. In the context of the video, the guest, Glebe, is a Senior Data Scientist at BCG, focusing on using data and machine learning to help brands and retailers connect with consumers in more personalized ways. His journey into data science began with a background in economics and statistics, highlighting that the field is accessible to those with diverse educational backgrounds.

💡Consulting

Consulting involves providing expert advice to individuals or organizations to help solve problems or improve performance. In the video, the guest discusses the unique opportunities and challenges of working in a data science management consulting firm, emphasizing the difference between traditional data science roles and consulting roles. Consulting requires a generalist attitude and the ability to apply data science skills across various industries.

💡Personalization

Personalization refers to the customization of products or services based on individual consumer preferences or behaviors. In the video, Glebe talks about the increasing importance of personalization in marketing and how data science can drive more targeted and effective outreach to consumers. This involves细微调整 (fine-tuning) of advertising content to enhance user engagement and loyalty.

💡Machine Learning

Machine Learning is a subset of artificial intelligence that involves the use of statistical models and algorithms to enable computer systems to learn from and make predictions or decisions based on data. In the context of the video, Glebe focuses on using machine learning to help brands connect with consumers, indicating the application of these techniques in data science for marketing and retail purposes.

💡Interview Process

The interview process refers to the series of assessments and conversations conducted by an employer to evaluate a job candidate's suitability for a role. In the video, Glebe discusses the differences between the interview process for data science roles in consulting firms versus industry roles, highlighting the importance of demonstrating a generalist attitude and analytical rigor in consulting interviews.

💡Client Readiness

Client readiness refers to the preparedness of a client or customer to engage with and benefit from a service or product. In the context of the video, Glebe talks about the importance of being client-ready in consulting roles, which involves the ability to interact effectively with clients, understand their needs, and communicate complex data science concepts in a way that adds value to their business.

💡Project Management

Project management is the practice of initiating, planning, executing, controlling, and closing projects to achieve specific goals and meet specific success criteria. In the video, Glebe discusses the structure of projects in consulting, where data scientists are responsible for delivering end-to-end solutions within tight timelines, requiring effective project management skills to ensure successful outcomes.

💡Data Systems

Data Systems refer to the integrated set of tools, processes, and methodologies used to collect, manage, analyze, and interpret data. In the video, Glebe talks about delivering data systems as part of his consulting work, which includes building the code base and surrounding processes that enable clients to maintain and use the system effectively.

💡Social Impact

Social Impact refers to the effect that a project, initiative, or organization has on society and the environment. In the video, Glebe highlights the importance of using data science for social good, such as improving healthcare decisions in developing communities, which can lead to better outcomes for vulnerable populations.

💡Personal Projects

Personal projects are individual endeavors undertaken outside of formal employment or education, often to develop skills, explore interests, or create something of value. In the video, Glebe encourages aspiring data scientists to work on personal projects that they are passionate about, as these projects can demonstrate their ability to apply data science skills to real-world situations.

💡Agile Methodology

Agile Methodology is a project management approach characterized by adaptability, collaboration, and the ability to respond quickly to changes. It involves breaking down work into small, manageable pieces or 'sprints' and iteratively refining the product. In the video, Glebe mentions writing code in an agile way to ensure that deliverables are deployable early in the project timeline, allowing for adjustments and feature prioritization as the project progresses.

Highlights

Consulting prides in helping clients look good rather than seeking credit.

Glebe Dropkoff, a senior data scientist at BCG, discusses his non-traditional path into data science from economics and statistics.

The importance of hands-on experience with data and modeling early in one's career.

Self-teaching through online classes and personal projects was key to Glebe's transition into data science.

The interview process for data science roles in consulting firms focuses on broad skill sets rather than subject matter expertise.

Client readiness and the ability to work with external clients is crucial in consulting roles.

The structure and team organization in consulting data science versus industry data science roles.

Consulting projects are shorter and require delivering analytical models within a紧凑的时间框架.

The transition from traditional management consulting to data science consulting, including maintaining and deploying systems.

The types of deliverables in consulting data science, such as dashboards and APIs.

Organizational structure of consulting teams and the role of data scientists within BCG.

The concept of modules in consulting projects and their role in scoping and delivering work.

The importance of planning and scoping in data science projects, both personal and professional.

Personalization and targeted marketing as a common use case for data scientists.

The role of data science in social good and non-profit organizations.

Advice for aspiring data scientists: focus on learning, stay client-ready, and consider volunteering for NGOs.

Transcripts

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Related Tags

Data Science Consulting Insights Career Development Project Management Personalization Social Impact Interview Insights Industry Trends Data-Driven Strategies Healthcare Analytics