LYLO jobs - Data Scientist

We are looking for a co-founder to lead our data strategy.

(WE ARE LOOKING FOR MINIMUM 5-6 YEARS EXPERIENCE IN THE FIELD OF DATA SCIENCE. EX-FOUNDERS PREFERRED)

Very generous co-founder level equity available- based on the candidate's past experience and overall contribution to the business.

Lylo uses machine learning technology to send customized catalogues to a customer every month. We are looking for someone who is passionate about disrupting women's fashion and lifestyle space using data science.

The team is being led by a 2X entrepreneur with one succesful exit.

You will be joining as a project lead where you will be evaluated for a couple of months, post which, if things go well, you will be joining the team full time.

If you are currently employed, it is fine as only a couple of hours of work per day is required at this stage.

The critical part will be your belief in the business and synergy with the rest of the team.

The team is based out of Delhi, though location is not a factor. Work can be completely remote.

Qualifications for Data Scientist

• Ex Founder is strongly preferred
• Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
• Experience working with and creating data architectures.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
• A drive to learn and master new technologies and techniques.
• We’re looking for someone with 5-7 years of experience and is familiar with the following software/tools:
• Coding knowledge and experience with several languages: C, C++, Java,
• JavaScript, etc.
• Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
• Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
• Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.
• Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
• Experience analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, etc.
• Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.