Senior Data Scientist

Job requirements:

Lead and manage a team of data scientists that is innovative, collaborative, and customer-focused

Hands on involvement in development and deployment of data science components

Identify and develop strategies and solutions to solve business problems

Customer focused product development by using programmatic logic, data driven approaches and machine learning algorithms

Actively deploying machine learning models in real-world production environments, ensuring scalability, performance, and reliability

Analytically approach problems and quantify KPI’s statistically Spots and manages data development challenges in the organization

Responsible for data science professional development of the team and training in the use of modern data science tools.

Create models to understand and help the business create improved customer journeys, better go-to-market plans, and product changes

Performs research and builds core understanding of the company performance metrics to qualitatively inform and interpret models.

Standardizes methods and algorithms used across the business unit.

Develops and maintains standard software libraries and associated documentation

Collaborate cross-functionally with product teams, engineering teams, and key stakeholders dedicated to improving our customer experience

Background of the candidate:

Degree in Computer Science, Data Science, Mathematics, Statistics or similar field

5+ years of progressive relevant work Demonstrated capacity to lead/manage and mentor teams Expertise in traditional and advanced data science techniques and tools, including machine learning (supervised/unsupervised learning), and statistical analyses

In-depth knowledge of theoretical and computational methods in statistics, mathematics, and machine learning

Hands on experience in end-to-end module development including data collation, data munging, model training, model selection, analytics & delivery

Experience in dealing with large volumes of textual data and using NLP models and techniques at scale

Experience architecting data integrations and running data flows to support complex data initiatives

Proficient in the use of relational database systems (MS SQL, SQL Lite, MySQL), comfortable with Postgres, as well as good understanding how the DB engine works

Knowledge of cloud technologies (Google Cloud Platform, AWS, Azure, etc.)

Experience with one or more machine learning and NLP frameworks such as Scikit Learn, PyTorch, Spacy, Huggingface, Tensorflow, Pyspark, NLTK

Experience with ML interpretability packages (Eli5, SHAP, LIME and so on)

Experience with data visualization software and techniques (Power BI, Tableau, Seaborn)