Because of the routines we follow, we often forget that life is an ongoing adventure...and the sooner we realize that, the quicker we will be able to treat life as art: to bring all our energies to each encounter, to remain flexible enough to notice and admit when what we expected to happen did not happen. We need to remember that we are created creative and can invent new scenarios as frequently as they are needed.

– Maya Angelou

went to a grammarly fireside chat with a few directors in their data org

what you look for when hiring

• base technical skills: sql, python, data manipulation (least important)
• is this person going to make our business successful? is grammarly going to grow the user base, make money
• people who understand business problems, can frame problems and solve them, has applied acumen
• create experiments that can drive business

recommendations for early data career

• find right mentors, strong set of leaders and peers, that will teach you more than anything else
• exposure to multiple aspects of problem solving, not just technical stuff, but cross-collaborate with stakeholders, how to report status, how to link your output with business impact, communication skills
• look into the tech stack of the company, work on the latest stuff
• company size, startup vs large company? structure is important

day2day for new hire on data platforms

• ingestion side, infra to bring data in
• data governance, compliance
• data eng: data modeling, transformations
• analytics & ML: data cleanliness and quality and availability
• everyone is a strong SWE, systems engineer (infra), data engineer (DE)
• strong generalist SWE
• databricks

big vs small company

• think about impact vs prestige
• big: prestige (name brand + density of talent)
  • mentoring on best practices, how to function these huge machines
• small: impact
  • have stories that your work directly move X millions of revenue
• find a company that can give you impact (bullet points on resume) + prestige

role of data science evolving (skills?)

• understanding the business context, the space you're working in, the elements you need to solve that
• less on fundamentals of the models, more on the application
• foundational statistics and knowledge intuition
• when things don't go well, really understanding exp design, causality, foundations of statistics will matter the most

AI companies

• robust system to measure quality of AI, human eval, responsible AI metrics
• subject matter experts on staff, people that can inform the models, everything that AI does is informed by human choices, who is making the choices?
• what does the data infra look like? is that company investing in the data? serious test on companies investment on AI

most innovative + impactful project

• orchestration framework for potential accounts to reach out to for sales team (ml, experimentation, llms to produce content)
• 50b events a day, making informed decisions from this
• suggestion quality, what is quality?

grammarly in llm era

• huge advantage in user context across different services
• lots of potential to innovate, supercharged efforts
• creates interesting data for feedback mechanism for marketing

some interesting work i encountered on their engineering blog

• CoEdIT: State-of-the-Art Text Editing With Fewer Parameters | Grammarly
• Measuring Marketing Effectiveness in a Cookie-Less World | Grammarly

migraine was back in the morning and i couldn't do anything at all in the morning besides cook lunch. acetaminophen kicked in 3 hours after i took it at 11am and i could finally function for most of today. pretty cool that i got to meet alumnis from cohort 11. when i graduate, i'll be meeting future students too. and i'll be saying the same things and giving them advice.