Lead Software Machine Learning Engineer – Anomaly Detection
Job location
United States – Remote
Type
Full Time
Responsibilities:
You will have the chance to work closely with data scientists and engineers to bring innovative ideas to fruition on top of multiple cloud vendors.
You will be expected to embrace engineering best practices in design and coding, and you will be responsible for data pipelines and tools that enable data analysis on a large scale.
You will collaborate with engineering teams across the organization to integrate machine learning capabilities into various products.
Lead a team of engineers in the design, development, and deployment of anomaly solutions.
Develop novel machine learning and statistical algorithms for anomaly detection that can handle large volumes of data and identify complex patterns.
Design and implement scalable architectures for anomaly detection systems that can integrate with existing infrastructure.
Collaborate with data scientists to analyze large datasets and identify potential anomalies, trends, and correlations.
Train and validate machine learning models using large datasets and evaluate their performance on detecting anomalies.
Deploy and maintain anomaly detection systems in production environments, ensuring high availability and low latency.
Mentor junior engineers and provide technical guidance on anomaly detection solutions.
Stay up-to-date with the latest research and advancements in anomaly detection and apply this knowledge to develop innovative solutions
Requirements & Skills:
Experience: 5+ years of experience in anomaly detection, machine learning, or data science.
Proficiency in programming languages such as Python, R, Java, or Julia.
Experience with machine learning frameworks.
Knowledge of statistical modeling and hypothesis testing.
Familiarity with cloud-based technologies such as AWS, Azure, or Google Cloud.
Familiarity with working with distributed systems and building systems for scale.
Experience with data storage technologies, such as SQL databases, NoSQL databases, and data lakes
Bachelor’s Master’s or Ph.D. degree in Computer Science, Engineering, Statistics, or a related field.
Proven experience in leading teams and mentoring junior engineers.
Strong communication and collaboration skills.
Ability to work independently and make technical decisions.