Data Scientist

Key Skills & Competencies:

• Programming: Proficiency in Python and SQL, with experience in time series data manipulation, analysis, and automation.
• Data Analysis: Expertise in data wrangling, cleaning, transformation, and exploratory data analysis (EDA).
• Machine Learning: Hands-on experience with supervised and unsupervised learning, model evaluation, and hyperparameter tuning.
• Deep Learning: Basic understanding of neural networks and familiarity with frameworks like TensorFlow or PyTorch.
• Visualization: Ability to create insightful visualizations using tools like matplotlib, Plotly, or similar.
• Mathematics & Statistics: Strong foundation in probability, statistics, linear algebra, and hypothesis testing.
• Software Engineering: Familiarity with Git, modular coding practices, API integration, and basic CI/CD workflows.
• Cloud Platforms: Exposure to AWS, GCP, or Azure is a plus.
• Business & Communication: Ability to translate data insights into business value and communicate findings clearly to both technical and non-technical stakeholders.

Core Responsibilities:

• Develop models, algorithms, and analytics for assigned projects, ensuring technical soundness by applying solid engineering principles and adhering to business standards, procedures, and product/program requirements.
• Utilize state-of-the-art methodologies to perform tasks efficiently and effectively, including conducting research to explore and introduce new technologies in data acquisition and analysis.
• Rapidly prototype multiple approaches using Data Science, Artificial Intelligence, and Machine Learning concepts, applying sound judgment to select the most suitable method for full-scale development.
• Prepare comprehensive technical documentation throughout the development phase, aligned with engineering policies and procedures.

Key Skills & Competencies:

• Programming: Proficiency in Python and SQL, with experience in time series data manipulation, analysis, and automation.
• Data Analysis: Expertise in data wrangling, cleaning, transformation, and exploratory data analysis (EDA).
• Machine Learning: Hands-on experience with supervised and unsupervised learning, model evaluation, and hyperparameter tuning.
• Deep Learning: Basic understanding of neural networks and familiarity with frameworks like TensorFlow or PyTorch.
• Visualization: Ability to create insightful visualizations using tools like matplotlib, Plotly, or similar.
• Mathematics & Statistics: Strong foundation in probability, statistics, linear algebra, and hypothesis testing.
• Software Engineering: Familiarity with Git, modular coding practices, API integration, and basic CI/CD workflows.
• Cloud Platforms: Exposure to AWS, GCP, or Azure is a plus.
• Business & Communication: Ability to translate data insights into business value and communicate findings clearly to both technical and non-technical stakeholders.

Core Responsibilities:

• Develop models, algorithms, and analytics for assigned projects, ensuring technical soundness by applying solid engineering principles and adhering to business standards, procedures, and product/program requirements.
• Utilize state-of-the-art methodologies to perform tasks efficiently and effectively, including conducting research to explore and introduce new technologies in data acquisition and analysis.
• Rapidly prototype multiple approaches using Data Science, Artificial Intelligence, and Machine Learning concepts, applying sound judgment to select the most suitable method for full-scale development.
• Prepare comprehensive technical documentation throughout the development phase, aligned with engineering policies and procedures.