Position Overview: We are seeking a highly skilled and experienced Machine Learning Engineer with expertise in handling both supervised and unsupervised datasets. The ideal candidate will possess a broad understanding of popular machine learning algorithms and ensemble techniques, along with a proven track record of optimizing machine learning models for real-world applications. In addition, the candidate should be proficient in visualizing and comparing algorithm results to derive meaningful insights.
Responsibilities:

1. Develop and implement machine learning models for both supervised and unsupervised learning scenarios.
2. Work on a variety of datasets, ensuring data quality and preprocessing to extract meaningful features.
3. Utilize a wide range of machine learning algorithms and ensemble methods to create robust and accurate models.
4. Optimize machine learning models for performance, scalability, and efficiency.
5. Collaborate with cross-functional teams to understand business requirements and translate them into machine learning solutions.
6. Create visualizations to effectively communicate and compare algorithm results, aiding in decision-making processes.
7. Evaluate the legitimacy of data sources and distinguish between valuable information and noise.
8. Stay abreast of industry trends and advancements in machine learning to contribute innovative solutions.

Qualifications:

1. Master's or Ph.D. in Computer Science, Machine Learning, or a related field.
2. Proven experience in developing machine learning models for real-world applications.
3. Strong understanding of supervised and unsupervised learning techniques.
4. In-depth knowledge of a variety of machine learning algorithms and ensemble methods, with experience using SVMs, Random Forests, Linear Regression, KNN, Nave-Bayes, K-Means, PCA, NNs, RNNs, & CNNs.
5. Proficiency in optimizing machine learning models for performance and scalability, and familiarity with automated optimization algorithms.
6. Demonstrated experience in visualizing and comparing algorithm results.
7. Ability to assess the legitimacy of data sources and filter out noise.
8. Strong programming skills in languages such as Python, R, PySpark, Scala, or similar.
9. Excellent problem-solving and analytical skills.
10. Effective communication skills to collaborate with cross-functional teams.
11. Deep understanding of distributed data in cloud-based environments
12. Familiarity with Google Cloud AI Platform for building, deploying, and managing machine learning models.
13. Knowledge of Azure Machine Learning for building, training, and deploying machine learning models.
14. Understanding of AWS SageMaker for building, training, and deploying machine learning models.
15. Deep industry, local market, and broader market economic understanding as it affects model development & effectiveness.
16. Ability to test the validity and/or effectively of predictive ML model results.

    Preferred Skills:

    1. Experience with deep learning frameworks.
    2. Knowledge of cloud platforms such as AWS, Azure, or Google Cloud.
    3. Familiarity with big data technologies (e.g., Spark, Hadoop).
    4. Previous industry experience in [relevant industry/sector].
    Excellent ability to utilize Natural Language Processing for text-to-numerical conversions