The fourth edition of the C1 Connect Summer Webinar Series featured Nicole Shimer, Senior Investment Partner at Insight Partners on Thursday, July 9th.
Nicole joined the series to talk more about her career journey and to provide further insight on how data science is applied in venture capital.
Nicole studied a combination of Economics, Mathematics, and Computer Science as an undergrad and when she was deciding on her future career, she wanted a role in the tech world that would allow her to leverage these skills without having to do ‘hard coding’, leading to her role at Insight Partners (IVP) as a Senior Investment Partner.
Throughout the presentation Nicole provided some color on how venture capital and private equity work, shed light on different stages of investing, and explained how data drives Insight Partners’ investment decisions.
Before accepting audience questions, Nicole wrapped up her presentation by highlighting open data science opportunities at several IVP portfolio firms which interested candidates can apply for by joining Correlation One’s virtual talent platform, C1 Connect.
Below are some questions that Nicole answered throughout the live Q&A session following her presentation. You can see Nicole’s responses in the video below:
- What are the most important skills to cultivate to break into venture capital?
- Could you share some resources (i.e. newsletters, books, websites) that help you stay up-to-date with your industry?
- What are the most important factors that can lead a start-up to gain traction or momentum?
- What are some technology trends that you are interested in?
If you are interested in opportunities at IVP portfolio companies, please apply to C1 Connect here.
About Data Science @ Work
There is a transparency problem in the data talent market.
At C1 we work with thousands of data scientists, data analysts, and data engineers from around the world, and we often hear from job candidates that they are unsure how to evaluate different data career paths, do not know what skills they should focus on developing, and need some guidance on how to find their next data science job.
Across industries, companies are challenged to define the difference between a great data scientist, data analyst, and data engineer on job descriptions. This makes it difficult for candidates to understand what their day-to-day responsibilities will be, how certain jobs will impact their career trajectories, and how common job titles like ‘data scientist’ differ from one company to another.
This lack of transparency leads to a huge waste of time for both candidates and companies. Candidates adopt ‘spray and pray’ job application strategies, applying to hundreds of roles that have ‘data’ in their title. Talent teams are then forced to search through thousands of resumes to find great candidates who then must be triaged to the appropriate role search. Oftentimes, the interview process uncovers that though a candidate is an excellent data scientist, her goals and skills do not align with the role. This wastes the time of the applicant and Senior Data Scientists responsible for conducting late stage technical interviews.
We launched the C1 Connect Data Science @ Work webinar series to break down the communication barriers between hirers and the world’s best data scientists, data analysts, and data engineers. Each week, our C1 Connect community is invited to hear directly from data leaders who share background on their career journeys, what working in their industry means practically for data professionals, and some tips for navigating the job search (and if applicable, how they can pursue opportunities with their teams).
After each session, candidates are invited to raise their hand for feature opportunities on C1 Connect by sharing their C1 Connect Datafolios- brief profiles designed to communicate the skills, roles, aspirations, and project work specifically for data professionals. Using C1 Connect’s Talent Match Algorithm, we pass on qualified candidates who fit the profile for active opportunities to the proper next steps in the candidate selection process.