Job Requirements

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. 

Work Experience

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.