We are joined by two data scientists who have firsthand experience with layoffs. We’ll talk about how to negotiate severance packages, how to handle stress, strategies for job hunting post-layoff, and how to reduce risks in full-time employment. Working with Daliana on personal branding: https://forms.gle/heNuZzaHjaAMQwLu6 Her email: daliana@dalianaliu.com Guests: Susan Shu Chang: Linkedin: https://www.linkedin.com/in/susan-shu-chang/ Newsletter: susanshu.substack.com Sundar Swaminathan Linkedin: https://www.linkedin.com/in/sswamina3/ Website: https://www.sundarswaminathan.com/⁠ (00:00:00) Introduction (00:06:13) Severance Negotiation (00:20:29) Identity crisis (00:26:22) Job search after layoff (00:30:21) Networking (00:35:23) Risk at pre-seed startups (00:37:03) How should data scientists pick companies (00:40:43) What to ask hiring managers (00:45:01) Does GenAI change interview processes? (00:47:17) Are data science teams getting leaner? (00:48:56) Future of data science roles (00:50:37) Full time employment and job security (00:53:46) Benefits of full time jobs (00:58:14) Reduce risk of being laid off (01:00:43) How to sell yourself (01:02:43) How to plan your finances (01:05:09) How to become an independent consultant

Jenny Wu, former data analyst turned sales engineer at Hex, discusses the transition into a customer-facing role, traits of successful salespeople, and embedding career and personal goals for happiness. Topics include sales engineer vs data analyst, day-to-day tasks of a sales engineer, and advice on career breaks.

Barry McCardel, Founder and CEO of Hex, talks about the future of data science teams, integrating AI into data science workflows, building data apps for stakeholders, and the potential of conversational user experiences in data work.

Sid Sharan, Senior Data and Applied Scientist at Microsoft, discusses evaluating AI products, OpenAI API for sentiment analysis, data science team culture, collaboration between data scientists and product managers, dealing with burnout, and the role of product data scientists in the age of generative AI tools.

Jess Ramos, Senior Data Analyst at Crunchbase, discusses topics such as job hopping, SQL in the real world, data analyst vs data scientist, salary negotiation, and the future of data analytics. She shares her experience with job hopping, her day-to-day work as a data analyst, and how to talk data to stakeholders. Jess also discusses the importance of salary negotiation and the impact of LinkedIn on her career. The podcast concludes with a discussion about the future of data analytics and the speaker's online presence and courses.

Mehdi Noori, applied science manager at the Generative AI Innovation Center at Amazon, discusses his transition from civil engineering to data science, working on sports vertical projects at Amazon, the transferability of skills across industries, building trust and effective communication, and the role of luck in success. He also shares his experience getting into college in Iran, the power of quitting and moving on, and his work on an application called Margarabe, a conversational date planner. Lastly, he talks about his future career plans in sustainability and AI.

Former finance professional, Misty Arnold, discusses her decision to quit her job and become a farmer. She shares her experience transitioning from the corporate world to farm life, achieving financial sustainability, and embracing uncertainty. Misty reflects on the collaborative nature of her job, handling stress, and making connections in her new community. She debunks misconceptions about farming and highlights the convenience of modern farms. Misty encourages others to save money before pursuing a different path and allowing enough time to adjust to a new way of living.

Pan Wu is a senior manager of data science at Meta. We talked about why he moved from machine learning to product data science, projects he worked on at Uber, Linkedin, and Meta, and how he transitioned from IC to manager. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Pan’s LinkedIn: https://www.linkedin.com/in/panwu/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ (00:00:00) Introduction (00:01:30) Why he transitioned from MLE to product DS (00:07:38) Meta data scientists skill sets (00:15:49) When did his interest shifted from MLE to product DS (00:18:04) Is MLE more respected? (00:25:46) A/B testing deep dives in 3 steps (00:28:21) Built a tool at Linkedin (00:35:52) How to sell your project (00:41:07) Junior vs senior data scientist (00:43:24) From staff data scientist to manager (00:45:18) Explore being a manager (00:46:24) Cultures in Uber, Linkedin, TrueCar (00:52:09) Data science over the past 10 year (00:55:06) MLE vs DS fun and frustration (00:57:26) Product DS reality (00:59:10) Learning new skills (01:01:39) Mistakes he made (01:06:34) Future of data science (01:08:04) Will data scientists be replaced by AI (01:09:42) Three skills he looks for when hiring

Betty Zhang is a data scientist currently working at a cloud security company, previously she was a data scientist at Amazon Web Services. Today we’ll talk about her computer vision projects in Sports, data science use cases in cyber security, from business major to data scientist, what’s her experience working in startups vs big tech companies. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Betty’s Linkedin: https://www.linkedin.com/in/betty-zhang-0bb63731/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ (00:00:00) Introduction (00:01:21) Computer Vision Project in Sports at AWS (00:12:28) Challenges in computer vision (00:14:02) Time allocation for ML projects (00:15:22) 3 key skills for computer vision (00:17:20) From business analyst to ML engineer (00:18:14) How she got her data scientist job through Linkedin (00:21:32) How she got into Amazon (00:22:17) Three tech skills needed during Amazon interviews (00:26:11) Why she joined a Cyber Security startup (00:27:22) Three cybersecurity use cases (00:29:47) Anomaly detection (00:30:40) ML for cybersecurity (00:34:43) Tech stacks Amazon vs Startups (00:39:35) Startups vs big tech (00:45:56) Balance learning and impact (00:48:35) Advice for new data scientists

Che Sharma, former data scientist at Airbnb and founder of Eppo, discusses toxic behaviors in experimentation culture, A/B testing best practices, and A/B testing for ML models on The Data Scientist Show. Topics include statistical power, effect size, monitoring metrics, alternative methods to A/B testing, difference in differences method, and A/B testing in ML and AI.