Data Science Associate

The Data Science Associate is responsible for supporting the data science team in developing, implementing, and maintaining data driven solutions that support business objectives. This includes working with large datasets, developing data models, and analyzing data to provide insights and recommendations.

• Collaborate with the data science team to identify business needs and develop data-driven solutions to address them. Collect, clean, and preprocess large and complex data sets from various sources.
• Develop statistical models and predictive models to identify patterns and trends in data.
• Analyze data to identify insights and opportunities for business improvement.
• Communicate findings and recommendations to key stakeholders in a clear and concise manner. Use data visualization tools to communicate complex data in an easy-to-understand format.
• Stay up-to-date with industry trends and new developments in data science.

EXPERTISE AND QUALIFICATIONS

• Bachelor's or Master's degree in computer science, statistics, mathematics, or related field. With 2-5 years of experience
• Strong programming skills in languages such as Python or R.
• Experience with data manipulation and analysis tools such as SQL and Excel.
• Knowledge of statistical modeling and machine learning techniques.
• Familiarity with data visualization tools such as Tableau or Power BI.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills.

1. Programming: Proficiency in at least one programming language such as Python, R or SQL is essential. They should be able to write and understand code, manipulate and analyze data, and be familiar with common data structures and algorithms.

2. Statistics and Mathematics: A solid understanding of statistics and mathematics is important for data science. This includes knowledge of probability theory, statistical inference, and basic linear algebra.

3. Data wrangling: Data wrangling involves cleaning, transforming, and merging data from different sources. A data scientist should be proficient in using tools such as pandas, dplyr or SQL to manipulate and preprocess data.

4. Machine Learning: Familiarity with machine learning concepts such as supervised and unsupervised learning, regression, classification, and clustering is important. They should be able to build basic models and evaluate their performance.

Skills And Expertise

• 3 to 5 years of experience in software or data engineering roles.
• Big Data & Analytics: Strong with QSL skill (RDBMS).