Data Scientist Lead

hace 2 semanas


Bogota, Colombia Chiper A tiempo completo

**About Chiper**

Chiper is a tech Startup present in Colombia, Mexico, and Brazil. Through its e-commerce platform, it offers mass consumer products at direct manufacturer prices to independent businesses such as neighborhood stores, liquor stores, mini-markets, among others. Our purpose is to create bridges between low and middle-income people who are looking for better alternatives to buying their essential goods, their needs, and opportunities for traditional and obsolete models with technology, always thinking about that end-user and their alternatives. Our inspiration is the ultimate buyer sourcing from these independent businesses.

Chiper was born as a project in 2018 and in 2019 it was launched as a company. We have had accelerated growth and we seek to add to our team of entrepreneurs who dream of taking over the world, who enjoy an environment of uncertainty and co-creation. We want people who first of all want to be the best in what they do and also help their colleagues to be.

**Be part of a team where you will be**:
Lead the Data Science team by planning, designing, prioritizing, and executing different initiatives based on statistical and mathematical models that are computationally implementable, in order to address various needs coming from different areas of the company.

**Your principal responsibilities will be**:

- Foster the organizational culture by ensuring the proper execution within the ecosystem with the team, in order to align all stakeholders around the company's objectives and KPIs.
- Translate, plan, and prioritize business needs into statistical and/or mathematical models in order to generate efficiency in the different processes with which it interacts.
- Develop, maintain, and evolve the set of processes for data modeling and logic that support the different platforms of the company, taking into account technical and business needs in order to ensure their scalability and effectiveness.
- Implement statistical and/or mathematical models in programming languages to ensure their execution on the platform.
- Manage the team, recruiting the best talent, training and monitoring their performance in order to ensure the personal and professional growth of each individual and the scalability and massification of the data ecosystem.

**We are looking the next skills**:

- Managerial skills and strategic thinking.
- Modeling and trust in predictive and inferential data models through the use of Machine Learning and Deep Learning techniques that cover from implementation and theory to execution and optimization.
- Strong knowledge in DevOps technologies (Jenkins, CircleCI, Kubernetes, Docker).
- Knowledge of Big Data tools (Spark, Hadoop, etc.)
- Knowledge of Cloud tools (GCP, AWS).
- Git and GitHub
- Programming, algorithmic and data structures.
- Convex and stochastic optimization.
- Linear algebra, probability, statistics and calculus.
- SQL handling
- Programming languages Python, R, and Julia and their data packages.

**What you’ll find when working at Chiper**:
An environment rewarding on the we work hard with energy and passion for what we do. We try always seeing the positive side in every situation, we try to be stay humble and get work done no matter our position and level in the company. If you believe you match these values we look forward to meeting you

**For additional information on Chiper please visit**:

- PERSONAL DATA PROTECTION POLICY. THE APPLICANT expressly declares to know and agree with the Data Handling Authorization Policy, which was duly shown and informed, and which is in force in the Company and applicable to any person who voluntarily delivers their personal data and / or sensitive to CHIPER SAS In addition, THE APPLICANT expressly declares to know the web location of the CHIPER SAS Data Management Authorization Policy referred to here, and which in any case can be consulted for the _Personal Data Protection Policy for Colombia_ and for the _Personal Data Protection Policy for Mexico_


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