Data Scientist (Artificial Intelligence, Machine Learning)

What are the ongoing responsibilities of a Data ScientistData Collection and Preprocessing:

• Implement data collection strategies to gather relevant data from different sources.
• Clean, preprocess, and validate data to ensure accuracy and reliability for subsequent analysis.
• Assist in maintaining and enhancing data pipelines in collaboration with data engineering teams.

Statistical Analysis:

• Perform exploratory data analysis to identify trends, patterns, and insights within datasets.
• Apply basic statistical techniques to test hypotheses, validate assumptions, and draw conclusions from data.

Machine Learning and AI Model Development:

• Develop and optimize machine learning models to address business problems and improve processes.
• Explore and apply Generative AI techniques under the supervision of senior team members to contribute to innovative solutions.
• Assist in model evaluation, validation, and deployment, monitoring performance and making adjustments as needed.

Understanding Human Behavior for AI Applications:

• Analyze data related to human behavior to help develop models that predict and influence outcomes.
• Work with domain experts to incorporate insights into AI models, enhancing their relevance and accuracy.

Data Engineering Collaboration:

• Collaborate with data engineering teams to ensure smooth integration of models into existing data systems.
• Contribute to designing and implementing scalable data storage solutions.

Cross-functional Collaboration:

• Work with product management, marketing, and business stakeholders to understand requirements and provide data-driven insights.
• Communicate analytical concepts and insights effectively to non-technical stakeholders through reports and visualizations.

Continuous Learning and Innovation:

• Stay updated on the latest developments in data science, machine learning, and AI technologies.
• Experiment with new methodologies and tools to enhance project outcomes and expand your skill set.

What ideal qualifications, skills & experience would help someone to be Successful

• Master s or Bachelor s degree in Statistics, Mathematics, Econometrics, Computer Science, Engineering, or related disciplines
• 2-4 years of experience in data science, predictive modeling, and machine learning.
• Proficiency in Python, R, or SQL, with hands-on experience in data analysis and model development.
• Basic knowledge of Generative AI models and their applications
• Ability to translate business problems into analytical tasks.
• Skill in explaining statistical and machine learning techniques to business partners.
• Experience in creating data-driven stories and insights.
• Proficiency in handling large datasets, including data cleansing, manipulation, and mining.
• Capability to tackle ambiguous challenges with a problem-solving mindset.
• Curiosity and willingness to learn independently.
• Strong written and verbal communication skills.
• Effective organizational and planning abilities
• Ability to work well under pressure and adapt in a dynamic environment.
• Team-oriented approach with a capacity to work independently when needed.
• Strong interpersonal skills and the ability to build relationships with colleagues and stakeholders