How to Apply
Who Should Apply?
We are seeking highly motivated individuals to join MassMutual as junior data scientists in our Data Science Development Program (DSDP). 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.
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, therefore we are seeking 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.
We accept applications from both early career professionals and from graduating seniors with demonstrated work experience. 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
- Leadership and community involvement
- Strong communication skills as demonstrated through personal statement
- Supporting references
To supplement learning and development, members of the DSDP spend up to 3 years engaging in supplemental coursework available 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. If you've already completed a graduate degree program in a related field, the first-year development position is likely not the right fit for you and we encourage you to check out our other career opportunities.
- Spring Applications accepted January 1 to February 15, 2019
- Spring Interviews: February 27 to March 8, 2019
- Spring Decisions by March 22, 2019
- Full-time positions will start June 5, 2019
We are accepting application portfolios for the spring recruiting round starting on January 1, 2019. The portfolio should include the following:
- Resume: Please submit your most updated resume; one page maximum.
- Personal statement: Tell us about your hobbies, interests, and pursuits. How do your interests relate to the field of data science? What type of impact would you like to have in your workplace, community, and world through the field of data science? Please limit to approximately one page.
- Academics: An official or unofficial transcript or other details of your academic background or training.
- Research and Technical experience: Up to six examples that provide information about your research and practical experience in the realm of data science. Examples should include evidence of technical proficiency that could form the basis of fundamental data science skills. (If submitting group project work, please submit a text file explaining your individual contributions the project) Some examples include:
- Peer-reviewed articles in relevant disciplines
- Posters at relevant conferences, workshops, or colloquia
- Class project reports in relevant courses
- Non-peer-reviewed technical reports or memos
- Evidence of datafest or similar event participation or awards
- Relevant code snippets (that may support your prior research or projects)
- 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 (e.g. Google Summer of Code)
- 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 your proficiency level with each (basic, proficient, advanced, etc).
- References: At least two (2) references are required, and should be listed at the end of the Summary Sheet. Please provide name, title, contact information, 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.
- Step 1: Send a .zip file called _portfolio.zip to email@example.com with all of the materials listed above. Please ensure that your portfolio is _no larger than 10MB. We won't be running any of your code, so there's no need to include data sets.
- Step 2: To finalize application, those who submit portfolios must also complete a brief application form through MassMutual's employment portal, accessible here.
- Questions?: Email firstname.lastname@example.org