Global Investment Sourcing Entity Data Aggregation & Modeling-BC-516

Discipline(s) souhaitée: Génie - informatique / électrique, Génie, Informatique, Sciences mathématiques, Mathématiques, Statistiques / études actuarielles, Affaires, Sciences sociales et humaines, Science économique
Entreprise: Ekohe
Durée du projet: 6 months to 1 year
Preferred start date: As soon as possible.
Langue exigée: English
Emplacement(s): Vancouver, BC, Canada; Canada
Nombre de postes: 1-3
Rechercher dans les réseaux internationaux de Mitacs - cochez cette case si vous souhaitez recevoir des profils de chercheurs basés à l’extérieur du Canada: 

Au sujet de l’entreprise: 

At Ekohe, we believe in the positive, transformational power of technology. For 13 years, we've harnessed best-in-class strategy, design, and technical know-how to create innovative, elegant, and practical AI solutions for a variety of global organizations. From partnering with leading PE/VC's to multinational retail to USAID-backed development NGO’s to the worlds largest sporting event, we deliver AI and Machine Learning-driven automation that is both useful and impactful, benefiting lives daily. Locations: Canada, US, France, Japan, China


Veuillez décrire le projet.: 

Ekohe built a platform, currently in-market, helping Private Equity & Venture Capital firms find new companies to invest in. These firms are always seeking an edge on their market -- they hope to identify, elevate, and act on sourcing & origination opportunities faster than their competitors, using their unique investment criteria. They do this by finding companies that are growing faster than others.  Our goal is to create the most comprehensive and predictive data-set in the world to solve for this need, and put that data set into action using the latest research.

We have been successful in applying our AI/ML models to make personalized recommendations (i.e. signals and scores), providing insights to clients on which companies are growing fastest. Our platform synthesizes and aggregates data inputs from a variety of sources, to identify both direct measures and proxies for company growth (i.e. we currently use employee count, web traffic, social media followers, net promoter score from 10+ data sources).

In our product roadmap, we hope to incorporate additional understandings of direct/indirect measures of company growth - e.g. peer-reviewed articles and academic papers, conferences, news mentions, financials (most critical - operating costs and revenue growth), customer and operation metrics, market growth, publicity (both positive & negative), office expansion, etc.

We need research help on 2 fronts: (1) Come up with the hypotheses: What are the measures of growth? How feasible is it to capture measures available in the public domain? (2) Test the hypotheses: Research and locate data, aggregate and synthesize it; create and implement data science models to determine whether they are indeed proxies for growth.



Expertise ou compétences exigées: 

We hope to obtain researchers with advanced expertise and experience in the following areas (ideally MA/PhD/post-doc+):

  • Statistics
  • Data Science
  • Data Mining
  • Machine Learning
  • Python

We do not necessarily need a researcher with all of these capabilities -- deep expertise in any single or multiple areas would be great.