Data Science Specialist

Job Summary:

The Specialist - Software Development (Artificial Intelligence) leads the design, development, and implementation of AI and machine learning solutions that address complex business challenges. This role requires expertise in AI algorithms, model development, and software engineering best practices. The individual will work closely with cross-functional teams to deliver intelligent systems that enhance business operations and decision-making.

Key Responsibilities:

• AI Solution Design & Development:

o Lead the development of AI-driven applications and platforms using machine learning, deep learning, and NLP techniques.

o Design, train, and optimize machine learning models using frameworks such as TensorFlow, PyTorch, Keras, or Scikit-learn.

o Implement advanced algorithms for supervised and unsupervised learning, reinforcement learning, and computer vision.

• Software Development & Integration:

o Develop scalable AI models and integrate them into software applications using languages such as Python, R, or Java.

o Build APIs and microservices to enable the deployment of AI models in cloud environments or on-premise systems.

o Ensure that AI models are integrated with back-end systems, databases, and other business applications.

• Data Management & Preprocessing:

o Collaborate with data scientists and data engineers to gather, preprocess, and analyze large datasets.

o Develop data pipelines to ensure the continuous availability of clean, structured data for model training and evaluation.

o Implement feature engineering techniques to enhance the accuracy and performance of machine learning models.

• AI Model Evaluation & Optimization:

o Regularly evaluate AI models using performance metrics (e.g., precision, recall, F1 score) and fine-tune them to improve accuracy.

o Perform hyperparameter tuning and cross-validation to ensure robust model performance.

o Implement methods for model explainability and transparency (e.g., LIME, SHAP) to ensure trustworthiness in AI decisions.

• AI Strategy & Leadership:

o Collaborate with business stakeholders to identify opportunities for AI adoption and develop project roadmaps.

o Provide technical leadership and mentorship to junior AI developers and data scientists, ensuring adherence to best practices in AI development.

o Stay current with AI trends and research, introducing innovative techniques and tools to the team.

• Security & Ethical Considerations:

o Ensure AI models comply with ethical guidelines, including fairness, accountability, and transparency.

o Implement security measures to protect sensitive data and AI models from vulnerabilities and attacks.

o Monitor the performance of AI systems in production, ensuring they operate within ethical and legal boundaries.

• Collaboration & Cross-Functional Support:

o Collaborate with DevOps teams to ensure AI models are deployed efficiently in production environments.

o Work closely with product managers, business analysts, and stakeholders to understand requirements and align AI solutions with business needs.

o Participate in Agile ceremonies, including sprint planning and retrospectives, to ensure timely delivery of AI projects.

• Continuous Improvement & Research:

o Conduct research and stay updated with the latest developments in AI and machine learning technologies.

o Evaluate new tools, libraries, and methodologies to improve the efficiency and accuracy of AI model development.

o Drive continuous improvement initiatives to enhance the scalability and robustness of AI systems.

Required Skills & Qualifications:

• Bachelor's degree in computer science, Data Science, Artificial Intelligence, or related field.

• 5+ years of experience in software development with a strong focus on AI and machine learning.

• Expertise in AI frameworks and libraries (e.g., TensorFlow, PyTorch, Keras, Scikit-learn).

• Proficiency in programming languages such as Python, R, or Java, and familiarity with AI-related tools (e.g., Jupyter Notebooks, MLflow).

• Strong knowledge of data science and machine learning algorithms, including regression, classification, clustering, and deep learning models.

• Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) for deploying AI models and managing data pipelines.

• Strong understanding of data structures, databases, and large-scale data processing technologies (e.g., Hadoop, Spark).

• Familiarity with Agile development methodologies and version control systems (Git).

Preferred Qualifications:

• Master's or PhD in Artificial Intelligence, Machine Learning, or related field.

• Experience with natural language processing (NLP) techniques (e.g., BERT, GPT, LSTM, Transformer models).

• Knowledge of computer vision technologies (e.g., CNNs, OpenCV).

• Familiarity with edge computing and deploying AI models on IoT devices.

• Certification in AI/ML or cloud platforms (e.g., AWS Certified Machine Learning, Google Professional Data Engineer).