Data Scientist

Data Processing & Management:

● Design, develop, and maintain scalable data processing pipelines for handling

● large datasets using both on-premise and cloud platforms (e.g., AWS).

● Ensure data quality, consistency, and accuracy through rigorous validation and cleansing processes.

Data Analysis & Insights:

● Monitor and perform comprehensive data analysis to identify trends, patterns, and correlations within large datasets. Generate actionable insights and present findings to stakeholders through clear and concise visualizations.

Model Development:

● Develop, test, and deploy predictive models with (deep) machine learning

algorithms using frameworks such as TensorFlow, PyTorch, and Scikit-learn.

● Continuously monitor and refine models to improve performance and accuracy.

Collaboration and Support:

● Work closely with data engineers, AI engineers, and software developers to

understand data requirements and provide technical support.

● Facilitate effective communication and collaboration within the AI and data teams

and other technical teams.

Security and Compliance:

● Ensure the security and compliance of data handling and processing by

implementing best practices and adhering to regulatory requirements.

● Conduct regular audits and assessments to identify and mitigate security

vulnerabilities.

Continuous Improvement:

● Identify areas for improvement in data processing, analysis, and model

● development workflows.

● Stay updated with the latest industry trends and technologies related to data

● science and AI.

Job Requirements

Education:

● Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field. A higher degree is a plus.

Experience:

● Minimum of 3-5 years of experience in data science, machine learning, or a similar role.

● Proven experience in processing and analyzing large datasets.

● Strong background in Python programming and data manipulation libraries (e.g., Pandas, NumPy).

Skills:

● Proficiency in machine learning frameworks and tools (e.g., TensorFlow, PyTorch, Scikit-learn).

● Strong analytical and problem-solving skills with a meticulous and data-sensitive

approach.

● Excellent communication skills with the ability to convey complex data insights to non-technical stakeholders.

● Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and big data technologies (e.g., Apache Spark, Hadoop).

● Attention to detail and a patient, methodical approach to data handling and analysis.

Preferred Qualifications:

● Experience in fraud detection, recommendation systems, and predicting customer behavior.

● Understanding of data engineering and MLOps methodologies.

● Awareness of security best practices in data environments.

● Proficiency with data visualization software (e.g., Tableau, Power BI).