The Client Machine Learning Engineer will be accountable and holistically leads the designs and builds of advanced ML models aiding in strategic business initiatives. Collaborates with cross-functional teams to understand business requirements then develop, train and optimizes machine learning models for diverse applications. The candidate needs the ability to conduct rigorous evaluation and validation of their models with expertise in deploying and integrating them with existing systems. Will assist in establishing monitoring systems for ongoing model performance and maintenance. Must also document development and deployment processes for knowledge sharing. The candidate should be passionate about staying up-to-date with the latest advancements in the field and applying that knowledge to real-world challenges.

Responsibilities:

Algorithm Development:

Design, develop, and implement machine learning algorithms to address specific business challenges.

Collaborate with cross-functional teams to understand requirements and deliver solutions that meet business objectives.

Data Analysis and Modeling:

Perform exploratory data analysis to gain insights and identify patterns in large datasets.

Build, validate, and deploy machine learning models for predictive and prescriptive analytics.

Feature Engineering:

Extract and engineer relevant features from diverse datasets to enhance model performance.

Optimize and fine-tune models for improved accuracy and efficiency.

Model Evaluation and Deployment:

Conduct thorough evaluation of machine learning models using appropriate metrics.

Deploy models into production environments, ensuring scalability, reliability, and performance.

Collaboration and Communication:

Work closely with cross-functional teams, including data engineers, software developers, and business stakeholders.

Communicate complex technical concepts to non-technical stakeholders effectively.

Continuous Learning:

Stay abreast of the latest developments in machine learning, data science, and related fields.

Contribute to a culture of innovation by proposing and implementing new ideas and approaches.

Qualifications:

Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field.

5-6 years of hands-on experience in developing and deploying machine learning models.

Proficiency in programming languages such as Python, R, or Java.

Strong understanding of forecasting revenue ERP environments (e.g.: Salesforce SAP ECC)

Strong knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn).

Experience with data preprocessing, feature engineering, and model evaluation techniques.

Familiarity with cloud platforms (e.g., Azure AWS) and containerization tools (e.g., Docker, Kubernetes).

Excellent problem-solving and critical-thinking skills.

Effective communication and collaboration skills.

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ML Engineer