Data Scientist (Artificial Intelligence, Machine Learning)

What are the ongoing responsibilities of a Data Scientist Data 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 Skills: Machine Learning, Data Science, Artificial Intelligence, Sql, Data Analysis, Python Experience: 2.00-4.00 Years