Data Scientist

Responsibilities

• Acquire a deep understanding of business problems and translate them into appropriate technical solutions.
• Design, develop, and implement end-to-end AI/ML pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment.
• Act as a full-stack data scientist to develop and deliver advanced analytics models, including classification, time series, LLM, and more.
• Write clean, efficient, and well-documented code in Python for data manipulation, feature engineering, and model development.
• Utilize SQL extensively for data extraction, transformation, and loading (ETL) from various relational databases.
• Collaborate internally and with data engineers to identify new and novel data sources, ensure robust data infrastructure, and explore their potential use in developing actionable business results.
• Monitor and maintain deployed AI/ML models, ensuring their ongoing performance, accuracy, and reliability.
• Analyze and interpret complex datasets to identify trends, patterns, and insights that inform model development and business decisions.
• Communicate technical concepts and analytical results effectively to both technical and non-technical stakeholders.
• Work independently with minimal guidance, taking ownership of projects and delivering results.
• Foster a collaborative team environment, showing the highest respect for team members and their contributions.
• Stay updated with the latest advancements in AI, machine learning, and data science technologies.
  

Qualifications

• Master's degree (M.S.) in Data Science, Computer Science, Business Analytics, Machine Learning, Statistics, or a related quantitative field.
• 3+ years of experience in AI/ML, with proven experience developing and deploying machine learning models in a production environment.
• Strong proficiency in Python for data science and machine learning.
• Expertise in SQL for data querying, manipulation, and database interaction.
• Strong skills in data acquisition, algorithm design, and model development and refinement.
• Experience with big data technologies, cloud-based data platforms (e.g., GCP, AWS), and business intelligence tools (e.g., Power BI, Streamlit, Plotly Dash).
• Solid understanding of machine learning algorithms, statistical modeling, and data analysis techniques.
• Excellent oral, written, and interpersonal communication skills.