Description:
Advanced Datalytics is collaborating with a promising startup in the sports sector to revolutionize the industry through innovative machine learning applications. We are seeking a highly skilled Full Stack Data Scientist / Machine Learning Engineer to design, develop, and implement machine learning solutions from scratch.
Key Responsibilities:
- Machine Learning Solution Development:
- Design, develop, and implement machine learning models and algorithms from the ground up.
- Apply supervised and unsupervised learning techniques to solve complex problems.
- Data Pipeline Engineering:
- Understand and enhance existing codebases to design and implement data pipelines.
- Curate and preprocess training data for optimal model performance.
- Business Integration:
- Collaborate with product teams to understand business use cases.
- Hypothesize and implement machine learning solutions that enhance user experience and drive business traction.
- Cloud and MLOps Expertise:
- Utilize cloud-based machine learning platforms (AWS, GCP, Azure) for scalable solutions.
- Implement MLOps practices for efficient deployment and monitoring of models.
- Cost-Effective Solutions:
- Design solutions with a focus on cost efficiency without compromising quality.
- Rapid Prototyping and Testing:
- Develop quick prototypes to test hypotheses and iterate based on feedback.
- Documentation:
- Document workflows, methodologies, and findings for knowledge sharing and reproducibility.
- Technical Expertise:
- Work with technologies like NLP, deep learning, reinforcement learning, and transfer learning.
- Utilize frameworks such as PyTorch, TensorFlow, Keras, and scikit-learn.
- Design and manage databases to support data storage and retrieval needs.
Qualifications:
- Education:
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field.
- Experience:
- Minimum of 5 years of experience in machine learning engineering or data science roles post graduation.
- Proven track record of building and deploying machine learning models in production.
- Technical Skills:
- Strong understanding of supervised and unsupervised machine learning algorithms.
- Proficiency in Python and related libraries (PyTorch, TensorFlow, Keras, scikit-learn).
- Experience with NLP, quantitative analysis, deep learning, reinforcement learning, and transfer learning.
- Solid understanding of cloud-based machine learning platforms (AWS SageMaker, Google AI Platform, etc.).
- Familiarity with MLOps tools and best practices.
- Experience in database design and management (SQL and NoSQL databases).
Soft Skills:
- Excellent problem-solving and analytical skills.
- Strong communication skills to explain complex concepts to non-technical stakeholders.
- Ability to work independently and collaboratively in a fast-paced environment.
- Detail-oriented with strong organizational skills.