Data Analytics

Job Description

Responsibilities related to proposing and selecting AI/ML models, curating and preparing data, and training AI/ML models, a candidate should possess a range of skills. Here’s a breakdown:

Responsibilities

Technical Skills:

• Machine Learning Algorithms: Proficiency in various ML algorithms (e.g., regression, classification, clustering, deep learning) and their applications.
• Programming Languages: Strong programming skills in languages commonly used in data science, such as Python, R, or Java.
• Data Manipulation and Analysis: Experience with data manipulation libraries (e.g., Pandas, NumPy) and data visualization tools (e.g., Matplotlib, Seaborn).

Data Preparation and Curation:

• Data Quality Assurance: Skills in data cleaning, preprocessing, and ensuring high data quality.
• Feature Engineering: Ability to identify and create relevant features from raw data to improve model performance.
• Database Knowledge: Familiarity with SQL and database management systems to extract and manipulate data.

Model Training and Optimization:

• Model Evaluation Metrics: Knowledge of how to evaluate model performance using appropriate metrics (e.g., accuracy, precision, recall, F1 score).
• Hyperparameter Tuning: Experience with techniques for optimizing model parameters (e.g., grid search, random search).
• Deployment Knowledge: Understanding of how to deploy models in production environments, including familiarity with tools like Docker or cloud platforms (e.g., AWS, Azure).

Analytical Skills:

• Critical Thinking: Ability to assess business challenges and determine the most suitable AI/ML solutions.
• Statistical Knowledge: Understanding of statistical concepts that underpin ML algorithms.

Interpersonal Skills:

• Collaboration: Strong teamwork skills to work with cross-functional teams (e.g., data engineers, business analysts, stakeholders).
• Communication: Ability to explain complex concepts in a clear and concise manner to non-technical stakeholders.
• Project Management: Skills to manage projects, prioritize tasks, and meet deadlines.

Continuous Learning:

• Adaptability: Willingness to stay updated with the latest trends and advancements in AI/ML technologies.
• Curiosity: A strong desire to explore new methods and tools for solving business problems with AI/ML.