How To Convert Data Science And Machine Learning Internships Into Jobs

Can popular massive open online courses turn into job offers? This a common dilemma faced in data science forums about internships and certificate courses converting in job offers. Now that you snagged an internship, built a portfolio of work, networked with the mid and senior management team and are ready to pursue a career in data science, you are waiting for the high-paying job offer to land in your inbox. According to UC Berkeley data scientist Karsten Walker, recruiters look for specific traits such as applying scientific methodology to a business problem and look for candidates who have a demonstrated history of applying analytical concepts. Breaking into the data science industry is tough as it is, and data science newbies face intense competition in the search for jobs after internships. However, getting an internship means getting one foot inside the door and applying classroom learning to real world business problems. Another upside of the internship is that most organisations who look for interns, do it from an opportunistic point of view wherein interns can be converted into full-time hires. It is an informal way of recruitment and also helps in minimising the hiring cost to the company.

How Can One Maximise Their Internship Experience And Convert It Into A Job Offer:

• Talk about the opportunity given to work on all types of projects, including the ability to work with unstructured data and writing scripts to scrape websites
• Talk about the responsibility given as an intern and how the work delivered created value
• Stress about the experience of working alongside data scientists to productionize models and testing different algorithms
• Stress about the importance of your technical credentials with respect to the impact on business and general IT skills
• Emphasise on how hiring a senior professional could take six months and can be an expensive process, in the meantime, critical requirements can be filled with an entry level candidate

Note to Recruiters: Here’s Why You Should Hire Entry Level Professionals

Vin Vashishta, data science and machine learning strategist recently observed via LinkedIn that most organisations who look for PhD-level candidates usually end up using basic techniques like regression and decision trees for structured data. For these jobs, coding skills required are minimum and the result is often a report or visualisation. Vashishta poses a relevant question —  why are we still asking for a Master’s or PhD with over three years of experience for an entry level position? Data science interns usually demonstrate an eager learning ability most sought after by recruiters and startup founders who are keen on seeing the passion behind the project. Vashishta strongly urges organisations and businesses to hire more entry level talent instead of leaning towards mere bigger degree holders for their data science needs. He believes organisations should be more open to hire an aspiring data scientist with a BS/Certification/Online Program and one or two years’ experience in development or analytics. Vashishta’s thoughts on entry level professionals are, “You’re going to be amazed by the value they return in terms of hiring costs and ability they bring to the table to grow with the business needs.” Given how data science is becoming more business-centric and is being leveraged across domains, it will be helpful for entry-level professionals to build skills in both tech side such as engineering, systems engineering and web programming and also beef up learnings about the business domain such as presentation skills, working in big teams, cross-functional stakeholder management. This can only be gained on the job, so try being as participative in the internship experience as you can. All these soft skills fall in the data science job cluster and add value to business.

Tips For Data Science Interview

For tips on cracking the dreaded data science job interview, Vashishta points out in a blog post that it comes to two things — potential and goal. He explains that the employers ask if the candidate must be self-motivated and does the he/she have a reasonable plan to achieve their goals. He writes that applicants should spend some time understanding their minutes professional motivations and what drives you as a person or sets you apart from others. One must make sure to emphasise how the role fits into their career progression and how the company can help in achieving the career goals. Besides skill, recruiters look for skills such as machine learning and how it will shape your work can leave a strong impression on the recruiting team. Tier I leadership management looks for vision and strategy and how it can be tied to business outcomes, build new products and meet demands, so sharing your vision with senior management can help clinch the job.