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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:
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field.
- Strong understanding of data science concepts, statistical analysis, and machine learning algorithms.
- Proficiency in programming languages such as Python ( 1 to 2 Years) or R ( 1 to 2 Years) for data manipulation, analysis, and modeling.
- Familiarity with data visualization tools and libraries, such as Matplotlib or Tableau.
- Experience with data preprocessing, feature engineering, and data cleaning techniques.
- Basic knowledge of SQL for data querying and manipulation.
- Strong problem-solving skills and ability to think analytically.
- Excellent communication skills, with the ability to effectively present complex concepts and findings to non-technical stakeholders.
- Ability to work collaboratively in a team environment and contribute to team goals.
- Knowledge of cloud platforms, such as AWS or Azure, and experience working with big data technologies are desirable.
- Familiarity with version control systems like Git and knowledge of software development practices is a plus
- Basic Understanding of Machine Learning Operations (MLOps): Familiarity with the lifecycle of machine learning models from development to deployment and maintenance.
- Knowledge of Data Ethics and Privacy: Understanding of data privacy laws (like GDPR) and ethical considerations in data science.
- Adaptability to New Technologies: Ability to quickly learn and adapt to new data analysis tools and techniques as they emerge.
- Critical Thinking: Ability to approach problems critically and propose innovative solutions.
- Project Management Skills: Basic project management skills to manage tasks efficiently and meet deadlines.
Responsibilities:
- Assist in data collection, preprocessing, and cleaning to ensure data quality and usability for analysis.
- Perform exploratory data analysis to identify patterns, trends, and relationships in the data.
- Develop and implement statistical and machine learning models to solve business problems.
- Collaborate with senior data scientists to design and execute experiments, analyze results, and generate actionable insights.
- Conduct data visualization to effectively communicate findings and present insights to stakeholders.
- Assist in developing and maintaining data pipelines and workflows for data ingestion, transformation, and analysis.
- Stay abreast of the latest data science techniques, tools, and methodologies, and apply them to enhance analytics capabilities.
- Collaborate with data engineering and IT teams to ensure seamless integration and availability of data for analysis.
- Contribute to the development and improvement of data science workflows and best practices.
- Document and communicate the methodology, assumptions, and limitations of data models and analyses.
- Assist in Model Deployment: Aid in deploying machine learning models into production environments, ensuring they run efficiently and reliably.
- Participate in Code Reviews: Engage in code review processes to ensure quality and adherence to best practices.
- Data Quality Assurance: Regularly check data sources for integrity and accuracy.
- Continuous Learning: Commitment to continuous learning and staying updated with advancements in data science and related fields.
