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Student Assistant for RoR Software Development
Do you have experience working with RubyOnRails? Join us for our well-structured and -architected RoR project and develop it further (while being coached by experienced software engineers)
At the Auto-ID Labs ETH/HSG at the Chair of Information Management (D-MTEC), we work with several industry partners for our research projects. for one of which we aggregate data from external sources like Crunchbase and Angellist. Your would help us develop the project further, and maintain the IT setup (infrastructure, code quality).
We have a functional tool with a basic user interface, which is being used by several employees of our industry partner. The tool currently includes around 5’000 companies with about 150’000 structured properties with full version history including source. The tool already calculates and visualizes several descriptive statistics (e.g. visualization of geographic location of companies on a map using Google’s JS Maps API, distribution of funding volumes). One of the aims of the tool is to implement all features in a relatively abstract and reusable way (e.g. it would be trivial to add new descriptive statistics). The tool has been architected in a very clean way, to make the code easily understandable, easy to maintain and extend. For instance, several design patterns have been used for the data importers, statistics, or reporting.
The UI has been developed using Twitter’s Bootstrap framework to achieve a relatively presentable layout, but the focus clearly lies on data management (aggregation, enrichment/post-processing, visualization).
**(See attached PDF for details)**
At the Auto-ID Labs ETH/HSG at the Chair of Information Management (D-MTEC), we work with several industry partners for our research projects. for one of which we aggregate data from external sources like Crunchbase and Angellist. Your would help us develop the project further, and maintain the IT setup (infrastructure, code quality).
We have a functional tool with a basic user interface, which is being used by several employees of our industry partner. The tool currently includes around 5’000 companies with about 150’000 structured properties with full version history including source. The tool already calculates and visualizes several descriptive statistics (e.g. visualization of geographic location of companies on a map using Google’s JS Maps API, distribution of funding volumes). One of the aims of the tool is to implement all features in a relatively abstract and reusable way (e.g. it would be trivial to add new descriptive statistics). The tool has been architected in a very clean way, to make the code easily understandable, easy to maintain and extend. For instance, several design patterns have been used for the data importers, statistics, or reporting. The UI has been developed using Twitter’s Bootstrap framework to achieve a relatively presentable layout, but the focus clearly lies on data management (aggregation, enrichment/post-processing, visualization).
**(See attached PDF for details)**
**Exemplary Tasks**
- Implementation of features like email alerts / reports based on search queries (e.g. “New startups that offer payment services and are based in Switzerland”), individually defined by users
- More powerful search feature, e.g. using Elasticsearch (or similar)
- Introduce tests using rspec
- Several Machine Learning applications, e.g. classification into B2B / B2C companies
- Connection with external mail delivery service instead of SMTP (e.g. Mandrill)
- Setup of Splunk (or similiar) for visualizing performance (e.g. request response times) & business KPIs (e.g. number of companies in database over time)
- Possibly connect more data sources for aggregation
- Inclusion of redis/memcache or similar for better performance / more complex search queries and reports
**(See attached PDF for details)**
**Exemplary Tasks** - Implementation of features like email alerts / reports based on search queries (e.g. “New startups that offer payment services and are based in Switzerland”), individually defined by users - More powerful search feature, e.g. using Elasticsearch (or similar) - Introduce tests using rspec - Several Machine Learning applications, e.g. classification into B2B / B2C companies - Connection with external mail delivery service instead of SMTP (e.g. Mandrill) - Setup of Splunk (or similiar) for visualizing performance (e.g. request response times) & business KPIs (e.g. number of companies in database over time) - Possibly connect more data sources for aggregation - Inclusion of redis/memcache or similar for better performance / more complex search queries and reports