Job Requirements
Qualifications and Experience:
- Bachelor's degree in Computer Science or a related field.
- 1-2 years of experience in data analysis or data engineering.
Skills / Knowledge:
- Familiarity with programming languages like Python, R, or SQL.
- Solid understanding of statistical and machine learning concepts.
- Strong problem-solving and analytical skills.
- Excellent data visualization and presentation skills.
- Excellent communication and technical documentation abilities.
Behavioral Competencies:
- Strong leadership and mentoring abilities.
- Ability to work collaboratively and learn quickly.
- Planning & Organizing
- Flexibility & Adaptability
- Creativity & Innovation
- Well-developed interpersonal skills and excellent communications skills in English.
- Respect & Integrity
- Problem Solving & Decision Making
Job Description
The Junior Data
Science Engineer supports data analysis projects by building and maintaining
models, pipelines, and tools under the guidance of senior team members.
Key Accountabilities & Responsibilities
- Assist in designing, developing, and implementing advanced data models and predictive analytics to solve business problems and drive decision-making.
- Build, maintain, and optimize scalable and efficient data pipelines to facilitate seamless data integration and processing across the organization.
- Conduct comprehensive exploratory data analysis to uncover insights, trends, and patterns, and present findings in a clear and actionable manner.
- Support the implementation and optimization of machine learning algorithms, ensuring they meet performance, accuracy, and scalability requirements.
- Work closely with cross-functional teams, including business stakeholders and senior data scientists, to understand requirements and deliver tailored data-driven solutions.
- Develop and maintain thorough documentation of data processes, models, and methodologies to ensure reproducibility and transparency.
- Prepare detailed reports and dashboards to communicate insights and progress to stakeholders.
- Stay up-to-date with emerging trends, tools, and methodologies in data science and analytics.
- Actively contribute to improving team workflows and adopting innovative approaches to solving challenges.