Lead Data Scientist - Data Science

Role description

Title:

Required Technical Skillset:Language

A machine learning engineer is responsible for designing, implementing, and maintaining machine learning systems and algorithms that allow computers to learn from and make predictions or decisions based on data. The role typically involves working with data scientists and software engineers to build and deploy machine learning models in a variety of applications such as natural language processing, computer vision, and recommendation systems.

The key responsibilities of a machine learning engineer includes:

• Collecting and preprocessing large volumes of data, cleaning it up, and transforming it into a format that can be used by machine learning models.


• Model building which includes Designing and building machine learning models and algorithms using techniques such as supervised and unsupervised learning, deep learning, and reinforcement learning.


• Evaluating the model performance of machine learning models using metrics such as accuracy, precision, recall, and F1 score.


• Deploying machine learning models in production environments and integrating them into existing systems using CI/CD Pipelines, AWS Sagemaker


• Monitoring the performance of machine learning models and making adjustments as needed to improve their accuracy and efficiency.


• Working closely with software engineers, product managers and other stakeholders to ensure that machine learning models meet business requirements and deliver value to the organization.

Requirements and Skills:

• Mathematics and Statistics: A strong foundation in mathematics and statistics is essential. They need to be familiar with linear algebra, calculus, probability, and statistics to understand the underlying principles of machine learning algorithms.


• Programming Skills: Should be proficient in programming languages such as Python. The candidate should be able to write efficient, scalable, and maintainable code to develop machine learning models and algorithms.


• Machine Learning Techniques: Should have a deep understanding of various machine learning techniques, such as supervised learning, unsupervised learning, and reinforcement learning and should also be familiar with different types of models such as decision trees, random forests, neural networks, and deep learning.


• Data Analysis and Visualization: Should be able to analyze and manipulate large data sets. The candidate should be familiar with data cleaning, transformation, and visualization techniques to identify patterns and insights in the data.


• Deep Learning Frameworks: Should be familiar with deep learning frameworks such as TensorFlow, PyTorch, and Keras and should be able to build and train deep neural networks for various applications.


• Big Data Technologies: A machine learning engineer should have experience working with big data technologies such as Hadoop, Spark, and NoSQL databases. They should be familiar with distributed computing and parallel processing to handle large data sets.


• Software Engineering: A machine learning engineer should have a good understanding of software engineering principles such as version control, testing, and debugging. They should be able to work with software development tools such as Git, Jenkins, and Docker.


• Communication and Collaboration: A machine learning engineer should have good communication and collaboration skills to work effectively with cross-functional teams such as data scientists, software developers, and business stakeholders.