Become a Junior Data Scientist at MassMutual
We are always looking for highly motivated individuals to join MassMutual as junior data scientists and participate in our Data Science Development Program (DSDP).
We are looking for individuals who are comfortable working in a challenging, fast paced environment and are extremely self-motivated. Data science projects are often complex, so we hire individuals who are comfortable wrangling with unstructured, difficult problems and are comfortable thinking outside the box and taking initiative. Those who enjoy tackling complex tasks in a collaborative environment are well-suited to a position on our team.
Qualified applicants have backgrounds in computer science, statistics, mathematics, or related fields and are enthusiastic about pursuing a career in data science. Applicants must have solid quantitative and computational skills and have a working knowledge of languages such as R, SQL, or Python.
To supplement learning and development, members of the DSDP engage in coursework through the Five College consortium and graduate-level courses at UMass Amherst in Computer Science and Statistics while working full time. Only those up to the task of balancing rigorous coursework with challenging project work should apply.
We accept applications from early career professionals and graduating students from undergraduate and graduate programs.
Qualified applicants will demonstrate the following:
- Academic study or independent training in computational, quantitative, or technical fields, such as computer science or statistics
- Research and/or practical experience in related fields
- Technical expertise
- Demonstrated work experience through part time employment, internships, or professional endeavors
- Community engagement, involvement, and volunteerism
- Demonstrated leadership and change-making
- Strong communication skills as demonstrated through personal statement
How to Apply:
- We are no longer accepting portfolio applications for the 7th DSDP cohort (start date June 2020).
Application portfolios consist of:
Resume: One page maximum
Personal statement: We care about innovation, curiousity, leadership, and change-making. Tell us about a time you led an effort for change. Or a time you challenged the status quo in your workplace, school, or community. What was the problem? How did you lead change? How to did you manage challenges? What was the impact? Please limit to approximately one page.
Academics: Transcripts of coursework from your academic instritution and/or certificates of completion for other training (such as MOOC coursework).
Research and Technical Experience: Up to six examples that provide evidence of your technical, computational, and programming skills. Our work is technical and quantitative, so candidates should provide evidence of study, application, and proficiency in math, stats, and computer science. Please also share any research experience in the realm of data science. (If submitting group project work, please submit a text file explaining your individual contributions the project.)
- Code snippets in Python, R, or SQL
- Research papers or posters
- Data visualizations or dashboads you have developed
- Tehnical articles or reports
- Evidence of datafest or hackathon participation or awards
- The URL to your Kaggle profile, Github account with code repositories, or Stackoverflow or similar website profile
- URLs of websites you own or have maintained
- Evidence of open-source contributions
- Relevant class projects
Summary Sheet: Every portfolio submission should include a 1 to 3 page summary document that includes the following sections and information:
- Personal Info: Name, School(s), Degree Type (BA, MS, etc.), Major(s), Graduation Year(s), Phone Number, Email Address
- Relevant classes: List only your courses (and corresponding grades) which are most relevant to data science work
- Work experience: List up to four bullet points of past or current employment and work experience
- Leadership and Community Involvement: List up to four bullet points of leadership experience and community involvement
- Portfolio summary: List each project or data science example you've included in your portfolio (to demonstrate research and technical expertise) and a 1-2 sentence summary of each and what you learned from that project.
- Programming Proficiency: Please list each programming language you have familiarity with and the number of months/years you've been working with each one. Please only list languages for which you've included code snippets in your portfolio.
- References: At least two (2) references are required, and should be listed at the end of the Summary Sheet. Please provide name, title, phone, email, industry/company, and relationship for each reference. At least one reference must be a faculty member, and at least one must be a manager, employer, or industry partner. References should be able to speak to your self-motivation, persistence, and leadership, ad your potential to work in a fast-paced environment on a collaborative team.
- How did you hear about us?: We love to know how we're best connecting with candidates. Please let us know how you heard about this opportunity and any time you were able to connect or meet with us throughout the application process.
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