Artificial Intelligence Specialist
📍
Austin, TX
🧪
Associate
Responsibilities
Job Description:
Job Description:
- Algorithm Development: Design, develop, and implement machine learning algorithms and models to address business challenges and opportunities. Collaborate with cross-functional teams to understand requirements, define objectives, and develop solutions that meet stakeholder needs.
- Data Collection and Preprocessing: Collect, clean, and preprocess large volumes of structured and unstructured data from diverse sources. Perform exploratory data analysis (EDA) to gain insights and identify patterns, trends, and anomalies in the data.
- Feature Engineering: Extract and engineer relevant features from raw data to improve model performance and predictive accuracy. Select appropriate features and transform data to optimize model training and evaluation.
- Model Training and Evaluation: Train, validate, and optimize machine learning models using appropriate techniques and methodologies. Evaluate model performance using metrics such as accuracy, precision, recall, F1 score, and ROC AUC, and iteratively refine models as needed.
- Model Deployment: Deploy machine learning models into production environments, integrating them into existing systems and workflows. Monitor model performance in real-time, identify issues or drift, and implement necessary updates or improvements.
- Scalability and Efficiency: Develop scalable and efficient machine learning solutions that can handle large-scale data processing and analysis. Optimize algorithms and workflows for performance, speed, and resource utilization.
- Collaboration and Communication: Collaborate with data scientists, software engineers, and other stakeholders to drive projects forward. Communicate technical concepts and findings effectively to non-technical audiences, providing insights and recommendations for decision-making.
- Research and Innovation: Stay updated on the latest advancements in machine learning research, methodologies, and tools. Experiment with new techniques and technologies, conducting research and prototyping to explore novel solutions to complex problems.
- Education: Bachelor's degree or higher in computer science, engineering, mathematics, statistics, or a related field. Advanced degrees (e.g., Master's or Ph.D.) are preferred but not required.
- Experience: Minimum of 3-5 years of experience in machine learning engineering, data science, or a related role. Proven track record of developing and deploying machine learning solutions in real-world applications.
- Programming Skills: Proficiency in programming languages commonly used in machine learning, such as Python, R, or Julia. Experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, or Keras.
- Data Skills: Strong data manipulation and analysis skills, including experience working with large datasets and performing data preprocessing tasks. Familiarity with SQL, NoSQL, and big data technologies (e.g., Hadoop, Spark) is a plus.
- Machine Learning Techniques: Solid understanding of machine learning algorithms, techniques, and methodologies, including supervised and unsupervised learning, deep learning, reinforcement learning, and ensemble methods.
- Software Engineering: Knowledge of software engineering principles and best practices, including version control, code review, testing, and debugging. Experience with software development lifecycle (SDLC) methodologies such as Agile or Scrum.
- Problem-Solving Skills: Strong analytical and problem-solving skills, with the ability to understand complex problems, formulate solutions, and implement effective strategies to address them.
- Communication Skills: Excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams and stakeholders. Ability to convey technical concepts and findings clearly and concisely.