Data Scientist

Job Description

Job Summary:

We are seeking a talented and motivated Junior Data Scientist to join our data analytics team. As a Junior Data Scientist, you will work on various data projects, assisting in data analysis, modeling, and developing machine learning solutions. You will collaborate with senior data scientists and cross-functional teams to extract insights from data and contribute to data-driven decision-making. The ideal candidate should have a strong foundation in data science concepts, programming skills, and a passion for solving complex problems using data.

Qualifications:

1. Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or a related field. (Graduated in 2024 or before)
2. Strong understanding of data science concepts, statistical analysis, and machine learning algorithms with 1 to 2 years of experience.
3. Proficiency in programming languages such as Python or R for data manipulation, analysis, and modeling (1 to 2 years of experience).
4. Familiarity with data visualization tools and libraries, such as Matplotlib or Tableau.
5. Experience with data preprocessing, feature engineering, and data cleaning techniques.
6. Basic knowledge of SQL for data querying and manipulation.
7. Strong problem-solving skills and ability to think analytically.
8. Excellent communication skills, with the ability to effectively present complex concepts and findings to non-technical stakeholders.
9. Ability to work collaboratively in a team environment and contribute to team goals.
10. Knowledge of cloud platforms like AWS or Azure and experience working with big data technologies are desirable.
11. Familiarity with version control systems like Git and knowledge of software development practices is a plus.
12. Basic understanding of Machine Learning Operations (MLOps): Familiarity with the lifecycle of machine learning models from development to deployment and maintenance.
13. Knowledge of Data Ethics and Privacy: Understanding of data privacy laws (like GDPR) and ethical considerations in data science.
14. Adaptability to New Technologies: Ability to quickly learn and adapt to new data analysis tools and techniques as they emerge.
15. Critical Thinking: Ability to approach problems critically and propose innovative solutions.
16. Project Management Skills: Basic project management skills to manage tasks efficiently and meet deadlines.

Responsibilities:

1. Assist in data collection, preprocessing, and cleaning to ensure data quality and usability for analysis.
2. Perform exploratory data analysis to identify patterns, trends, and relationships in the data.
3. Develop and implement statistical and machine learning models to solve business problems.
4. Collaborate with senior data scientists to design and execute experiments, analyze results, and generate actionable insights.
5. Conduct data visualization to effectively communicate findings and present insights to stakeholders.
6. Assist in developing and maintaining data pipelines and workflows for data ingestion, transformation, and analysis.
7. Stay abreast of the latest data science techniques, tools, and methodologies, and apply them to enhance analytics capabilities.
8. Collaborate with data engineering and IT teams to ensure seamless integration and data availability for analysis.
9. Contribute to developing and improving data science workflows and best practices.
10. Document and communicate the methodology, assumptions, and limitations of data models and analyses.
11. Assist in model deployment: Aid in deploying machine learning models into production environments, ensuring they run efficiently and reliably.
12. Participate in code reviews: Engage in code review processes to ensure quality and adherence to best practices.
13. Data quality assurance: Regularly check data sources for integrity and accuracy.
14. Continuous learning: Commitment to continuous learning and staying updated with advancements in data science and related fields.