Overview

The Machine Learning/AI Analyst Specialist is responsible for designing, developing, and implementing data-driven solutions using advanced analytics, machine learning models, and artificial intelligence techniques. This role bridges data science, business strategy, and technical implementation—transforming complex datasets into actionable insights and intelligent systems that support organizational decision-making and automation.

Key Responsibilities

1. ML/AI Model Development

Build, train, and optimize machine learning and deep learning models for prediction, classification, clustering, NLP, or recommendation tasks.
Implement model validation, performance tuning, and continuous improvement using best-practice ML pipelines.
Use frameworks such as TensorFlow, PyTorch, Scikit-learn, XGBoost, or similar.

2. Data Analysis & Feature Engineering

Collect, clean, and preprocess structured and unstructured data from multiple sources.
Perform exploratory data analysis (EDA) to identify trends, patterns, and relationships.
Engineer features that enhance model accuracy and relevance.

3. AI Solutions & Deployment

Translate business requirements into scalable AI solutions.
Deploy models into production (APIs, cloud services, automation pipelines).
Collaborate with engineering teams to implement MLOps processes.

4. Reporting & Insights

Visualize complex data and model outcomes using dashboards (Power BI, Tableau, Looker, etc.).
Communicate analytical findings to technical and non-technical stakeholders.
Prepare documentation, reports, and presentations outlining methodologies and results.

5. Research & Innovation

Stay informed of emerging AI/ML trends, tools, and methodologies.
Evaluate and implement new technologies such as LLMs, generative AI, vector databases, and reinforcement learning.

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or related field.
Proven experience in machine learning, data analytics, and AI model development.
Strong proficiency in Python, SQL, and ML libraries.
Experience with cloud platforms (AWS, Azure, GCP) and ML lifecycle tools (MLflow, Kubeflow, SageMaker).
Solid understanding of statistics, probability, and algorithmic concepts.

Preferred Qualifications

Experience working with large language models (LLMs) and generative AI tools.
Knowledge of big data technologies (Spark, Databricks, Hadoop).
Familiarity with deep learning architectures (CNNs, RNNs, Transformers).
Background in MLOps practices and CI/CD pipelines.
Industry-specific experience (finance, healthcare, e-commerce, manufacturing, etc.).

Key Competencies

Analytical and critical-thinking skills.
Strong problem-solving abilities.
Excellent communication and data-storytelling skills.
Ability to work collaboratively in cross-functional teams.
Attention to detail and a mindset for continuous learning.

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