About Us: We are a Global Group AI team that empowers organization with citizen data scientists and enforces governance to ensure responsible and ethical use of AI. Our team is responsible for developing advanced analytics and machine learning models, and delivering data-driven insights to business leaders across the organization.
Job Description: As a Junior Data Scientist, you will be responsible for creating and implementing complex machine learning models, performing data analysis, and driving insights from large datasets. You will work with stakeholders across the organization to understand their business needs and translate them into data-driven solutions. You will also work closely with citizen data scientists to help them leverage data and build models for their business problems.
Key Responsibilities:
- Develop and implement machine learning models to solve complex business problems, using techniques such as linear regression, logistic regression, decision trees, and other supervised and unsupervised learning models.
- Perform data analysis and create insights from large datasets.
- Work with stakeholders across the organization to understand business needs and translate them into data-driven solutions.
- Collaborate with citizen data scientists to empower them to leverage data and build models for their business problems.
- Enforce governance to ensure responsible and ethical use of data.
- Keep up-to-date with the latest developments in machine learning and data science.
Requirements:
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Actuarial Science or related fields.
- At least 1-4 years of experience in data science or related field.
- Strong knowledge of machine learning algorithms and statistical modelling techniques, especially using Scikit-learn.
- Proficiency in Python, PySpark and SQL.
- Experience with data visualization and reporting tools (e.g. Power BI, QlikSense).
- Excellent communication and problem-solving skills.
- Experience with GenAI solutions and prompt engineering
Nice-to-haves:
- Familiarity with actuarial methods and models.
- Experience in MLOps and data pipelines eg, Bitbucket, Artifactory, Jenkins
- Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch).
- Experience with cloud-based services (e.g. AWS, Azure, GCP).
- Knowledge of big data technologies (e.g. Hadoop, Spark).
