Lead - Data Scientist - KPO

Lead Data Scientist

- The successful candidate will be responsible for ensuring the smooth operations of the data science team and will report directly to the CTO.

- Demonstrated industry experience of successfully building an end-to-end product based on machine learning is a must.

Management Responsibilities:

- Supervise a team of data scientists and senior data scientists

- Interact with the developers, QAs, and Dev-Ops teams to ensure a successful deployment of the product

- Interact with the UX, marketing, sales teams to gather the requirements for the product development

- Translate the requirements into actionable tasks for the data science team

- Assign tasks, oversee their correct implantation, revise code and ensure timely delivery of new features and updates

- Ensure the smooth running of the product in the live environment

Technical Responsibilities:

- Ensure the correct implementation and running of data collection via the use of third party APIs

- Design accurate and scalable machine learning models for forecasts and classification

- Define metrics and indicators to quantify business performance

- Integrate models and algorithms into the data pipeline and live environment

- Develop REST APIs using Python, Docker, and Flask

Job Requirements:

- Degree in Mathematics / Statistics / Physics or Data Science / Computer Science

- Higher education such as Master/Ph.D. or research experience preferred

- 8+ years of experience in a data scientist role

- Experience with end-to-end product development

- Experience with team management in the Agile framework

- Experience with the following technologies:

- Python

- NoSQL databases (MongoDB preferred)

- UNIX operating systems, ssh protocol, and bash scripting

- Git version control system

- Docker

- Develop REST APIs (Flask preferred)

- AWS deployment

- Good applied statistics skills, such as distributions, statistical testing, regression, etc.

- Excellent understanding of machine learning techniques and algorithms, such as Neural Networks, SVM, Decision Trees, Random Forests, clustering, etc.

- Demonstrated experience with common data science libraries, such as Pandas, NumPy, SciPy, matplotlib

- Experience with data science toolkits, such as Sci-kit learn or TensorFlow

- Experience with big data frameworks (Hadoop, Spark, etc) preferred

- Excellent verbal communication skills.

- Good problem-solving skills

- Attention to detail

- Ability to adapt to the needs of the organization and tea