Job Title:
Data Science Engineer
About The Role:
To apply data science techniques and machine learning algorithms to solve business problems, improve decision-making, and ensure the efficient deployment of models in production.
What Will You Do:
- Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
- Analyzing the ML algorithms that could be used to solve a given problem
- Exploring and visualizing data to gain an understanding of it
- Identifying differences in data distribution that could affect performance when deploying the model in the real world
- Verifying data quality, andoror ensuring it via data cleaning
- Supervising the data acquisition process if more data is needed
- Defining the preprocessing or feature engineering to be done on a given dataset
- Defining validation strategies
- Training models and tuning their hyperparameters
- Analyzing the errors of the model and designing strategies to overcome them
- Deploying models to production
What we are looking for:
- Bachelor's degree in Computer Science, Data Science, Mathematics, or a related field.
- 4+ years of experience in data science, machine learning, or related fields.
- Data Science or Machine Learning certifications (e.g., Google Professional Data Engineer, Microsoft Certified: Azure Data Scientist).
- Experience with specific data science platforms (e.g., AWS Sagemaker, Google AI Platform) is a plus.
Soft Skill Requirements:
- Strong problem-solving and analytical skills.
- Effective communication skills for presenting findings to stakeholders.
- Ability to work collaboratively in a team environment.
- Adaptability and a proactive approach to problem-solving.
Technical Skill Requirements:
- Proficiency in data science tools and languages (Python, R, SQL).
- Expertise in machine learning algorithms and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Strong knowledge of data processing, feature engineering, and model validation techniques.
- Experience with cloud platforms (e.g., AWS, GCP) and deployment of models to production.