Data Scientist

Qualifications

• Proficiency in the following domains is crucial:
  • Natural Language Processing (NLP) and/or Text Analysis
  • Neural Networks (CNN, LSTM)
  • Object Detection
  • Optical Character Recognition (OCR)
  • Document classification and processing automation
• Proven experience in classical machine learning techniques and strategies such as supervised and unsupervised learning, classification, regression, decision trees, clustering, etc.
• Proficiency in python and Data Science or Machine Learning suite of libraries (e.g. Scikit Learn, Pandas, Matplotlib, etc.)
• Proficiency in Data Analytics techniques (e.g. data collection, data wrangling, data cleaning, EDA, etc.)
• Proficiency in SQL and analytical tooling
• Proficiency in usage of different data platforms such as RDBMS, Data Warehouse and Data Lakes is a plus
• Experience in cloud platforms in cloud such as Google Cloud Platform and VertexAI for MLOps  (preferred) and / or Azure
• Experience in working and coordinating remotely
• Strong problem solving and solutions engineering mindset
• Strong analytical and organizational skills
• Preferably a degree in the related fields of Mathematics, Computer Science, Statistics

Responsibilities

• Utilize various techniques such as data collection, analytics, data visualization and work with vast amounts of data to produce pertinent insights that enable data-driven solutions for business.
• Utilize data to explore and create models that will help generate insights and recommendations, along with creating reports and visualizations as part of deliverables required by stakeholders such as product owners and senior management
• Effectively communicate the technical background, potential, and importance of the solutions in order to generate an appreciation of the work done; understand the needs of the stakeholders
• Readiness to conduct research and condense knowledge on relevant literature to map and direct the paths towards business solutions
• Ensure data handled are within data policies, standards and regulations in terms of compliance
• Be able to manage stakeholders’ expectations in terms of timelines and deliverables