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An ML & NLP backed easy-to-use solution that analyses user's personality, academic and professional experience, and technical skills through carefully-curated surveys and resumes. We provide personalized job recommendations by translating the person’s traits and future goals to matching career and corresponding course recommendations. 

• Taking a person’s persona into account: Combines personality and interests to recommend best career paths 
• Saving time in fast life: Saves time, energy, and frustration from million Google searches, thousand person-to-person advice and hundreds of dilemmas 
• Knowledge sharing among masses: Pools the knowledge graphs to enrich learning and expedite career growth
• Crunching GBs of information to personal needs: Once a person’s inherent talents, skillset, and career goals are identified, crunches information glut into relevant jobs, articles, courses and competitions
• Scalability and Extension: Potential extension includes growing the network and developing a mentorship program to cultivate meaningful relationships and guidance for those with fewer opportunities. 

As master's students from Columbia Engineering School, almost all our ‘smart’ peers struggle during jobs and internships search inducing deep stress, self-doubts, and inefficiency. Despite each carrying his/her own talents and having the intellect to make a real impact on society, the ‘confidence’ and ‘clarity’ in choosing industries, roles, and jobs are surprisingly lacking. One of the biggest reasons is the incapability to match their interests and skillsets with the real job market, and the other challenge is ‘time constraint’ with job search, applications, interview preparation along with coursework. They apply everywhere, prepare for every role and, hence, often fail losing more confidence. 

We think that it would be much more challenging for those who are not necessarily in school to advance or pivot their career. We realized that it is a great privilege to be able to talk to alumni of the program to learn more about the industries and consult with career officers and professors about our career interests and resources. We aim to offer personalised career support to driven individuals without these support and spread awareness about person-based career opportunities by introducing articles, recommending online courses and up-skilling guidance in the form on AI-backed digital platform.

Our product will combine personality, past experience, and future goals to identify the right jobs to target and recommend the right resources depending on the individual’s learning speed and progress. We utilize advanced data science techniques, such as machine learning and natural language processing to do so.

We are a robust Machine Learning-based digital platform that recommends career paths and learning resources to expedite and personalize the path to success. Personality traits and acquired skills coupled with academic and professional goals serving as a metric provide personalized digital mentorship. 

We will first combine survey data on more qualitative aspects that combine one’s personality with the resume to better understand the user in addition to identifying quantitative and technical skills. Then we match both the behavioral and technical skills to a job based on a recommendation algorithm. Depending on the user’s skill set, we also recommend appropriate courses at Columbia as well as online courses, resources, and competitions.

As mentioned in the previous question, our product serves driven, marginalized individuals who are interested in pursuing career in technical fields. Given the nature of the industry, technical skillsets and technical interviews are becoming more important than the name of the degree or the reputation of the school one attends. If we can recommend the right career and right resources to achieve their team and reduce the hassle, they can focus on developing their skillsets and grow as a data scientist. 

Our solution is a mix of challenges- Good jobs and inclusive entrepreneurship as well as the digital workforce challenge. Idea is to extract the information from professionals and students in STEM domain through surveys, resumes and current satisfaction rates. And then converting this information into an extremely useful, focussed and personalised AI-based recommender platform where every individual gets crunched resources, information, articles, competitions, scholarship opportunities and related stories to act as their role-models. We believe that this platform when offered to a curious child, even from a marginalised community, can do wonders. Career counselling and mentorship is next stage plan.

To our knowledge, there is no current platform that seeks to recommend jobs and resources from survey and resumes. We We think that it is the most effective solution with endless potential. 

The core technology will be our algorithm and the attributes created to quantify subjective and abstract parameters like personality traits, job satisfaction and happiness index. We will combine these parameters with the skillsets and experiences to derive a pattern that our model will learn to guide a new student. Firstly, analysing his/her level, deciphering his/her traits, interests and acquired skillsets, matching him/her with prospect successful role, job and industry types and then recommending resources for the same along with the informations on 'highly' relevant jobs posted, courses created and competitions announced. It will be an AI-NLP based model.

Here we answer to what change we are bringing into the world from the existing solutions.

LinkedIn is one of the biggest players in this area along with companies like Indeed, glassdoor. Why do we stand out and will make a difference: 

1) None of the existing players identifies a person on an individual/behavioral level (both from personality traits and learning capability standpoint) to help them decide the career path and solve dilemmas 

2) Job and course recommendations are based on individuals’ biases and perceptions 

3) LinkedIn, for instance, doesn’t provide means for personalized upskilling Overall, as automation and AI are entering businesses, the need to identify one’s hip-pocket skills and hone them is high. One cannot work in a digressed manner and yet achieve success. In the simplest words, we assist the individual in defining a clear picture of their ‘prospect’ career goals and assist in honing them in a focussed manner for focussed and fast growth. 

Stage 1: Columbia engineering graduate students: 3000+ 

Stage 2: Scaling it up for engineering students all across the United States: 500,000+ 

Stage 3: Adding dimensions like mentor-matching, unique and interesting mix of STEM skillset with fields like art, law, psychology etc. 

We are planning to launch the survey to capture data and then develop the platform and then launch the recommender system. Initially a course recommender for Columbia students and then a prospect role, job and industry types within their area of interest.

Data will be captured both from the students and the working professionals. It will assist in getting the satisfaction rates and then improving the model from the experiences of people. The goal within next year is to provide the career, course and resource recommender to all the Columbia graduates in the next one year and then scaling it to provide it to all STEM students all across the world. 

Scaling will need modification as per cultural, financial and geographical differences among students all across the globe. Also, we will be adding one-to-one mentorship and career counselling dimensions to the platform to further improve the quality of guidance.

Major barriers are financial, legal and market barrier. We, being international students in United States, cannot afford to work on an idea without any job due to student loans etc. Along with that there are many legal guidelines which we could be unaware of. Moreover, scaling the solution to global scale will entail taking into account many more attributes making the model more complicated. 

We believe in 'less is more' and hence, we will be working on making a good robust model that doesn't need to be changed as per markets, student types and countries and rather learns on its own with the dynamic technical and business industries.

2 people - Columbia Students

We are masters student in data science and surrounded by STEM students - so we experience the problem on root level. With resources that Columbia provides, smart peers, proficient professors, well-established alumni network and technical resources, we believe that our team is well-positioned to deliver this solution.

We are planning to launch the survey to capture data and then develop the platform and then launch it. Once piloted, tested and gained customers' trust, we are planning to generate revenue from May onwards. 1) Monthly operating expenditures - domain license ($42 max/year), 3-4 data scientists ($300/month), 1-2 web developers ($200/month) 2) Survey compensations

Around $5000 initially. 

We need more confidence, a great platform and of course, most importantly, financial assistance to convert our idea into reality.

MIT faculty or initiatives, or Solve Members