The Role
Develop and deploy production machine learning models for content classification and segmentation. Build multi-class and multi-label classifiers, perform exploratory analysis, feature engineering, model training, tuning, and evaluation, and create text- and metadata-based features using Python, SQL, pandas, and Spark. Develop scalable workflows, maintain tested and documented code, and collaborate with data scientists, engineers, and stakeholders.
Summary Generated by Built In
City: Santa Monica, CA & Seattle, WA
Onsite / Hybrid / Remote: Hybrid. Onsite Monday through Thursday. Remote Friday.
Duration: 18 months
Rate Range: Up to $103.50/hr on w2
Work Authorization: GC, USC, All valid EADs except OPT, CPT, H1B
Must Have:
- 5+ years of hands-on industry experience developing and deploying machine learning models in production.
- Strong Python programming skills, including pandas and scikit-learn.
- Hands-on experience with supervised machine learning, particularly classification models.
- Experience with Natural Language Processing (NLP), including text-based feature development.
- Strong experience with feature engineering, preprocessing, and model evaluation.
- Strong SQL skills for querying and analyzing large datasets.
Responsibilities
- Design, develop, and improve machine learning models for content classification and segmentation.
- Build classification models that automatically assign content categories, genres, and segments.
- Enhance existing machine learning models and expand classification coverage across different content types.
- Develop multi-class and multi-label classification models using supervised learning techniques.
- Perform exploratory data analysis, feature engineering, model training, tuning, and evaluation.
- Process structured and semi-structured datasets using Python, pandas, SQL, and Spark.
- Develop text-based and metadata-driven features to improve classification accuracy.
- Apply techniques such as categorical encoding, normalization, feature selection, and hyperparameter optimization.
- Build scalable machine learning workflows to support production applications.
- Collaborate with data scientists, software engineers, and business stakeholders to implement ML solutions.
- Maintain clean, modular, tested, and documented machine learning code.
Qualifications:
- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field, or equivalent practical experience.
- Minimum 5 years of relevant machine learning experience, with substantial hands-on model development.
- Strong understanding of classification algorithms, including logistic regression, decision trees, random forests, ensemble methods, and gradient-boosted models.
- Experience building, training, evaluating, and deploying machine learning classifiers.
- Experience with multi-class and/or multi-label classification.
- Experience with NLP techniques and text-derived features.
- Strong knowledge of cross-validation, model performance metrics, and hyperparameter tuning.
- Ability to independently deliver machine learning solutions from initial data exploration through production implementation.
- Experience with software development practices, including Git, testing, logging, and code reviews.
Nice to Have
- Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related field.
- Experience in media, entertainment, streaming, or digital content platforms.
- Experience building classifiers using content metadata, catalog data, or high-dimensional datasets.
- Experience with NLP techniques such as TF-IDF, text embeddings, and semantic representations.
- Experience with workflow orchestration tools such as Airflow or Luigi.
- Experience with AWS or similar cloud environments.
- Experience with search, recommendation systems, or content categorization.
Skills Required
- 5+ years of hands-on industry experience developing and deploying machine learning models in production
- Strong Python programming skills, including pandas and scikit-learn
- Hands-on experience with supervised machine learning, particularly classification models
- Experience with Natural Language Processing and text-based feature development
- Strong experience with feature engineering, preprocessing, and model evaluation
- Strong SQL skills for querying and analyzing large datasets
- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field, or equivalent practical experience
- Experience building, training, evaluating, and deploying machine learning classifiers
- Experience with multi-class and/or multi-label classification
- Strong knowledge of cross-validation, model performance metrics, and hyperparameter tuning
- Ability to independently deliver machine learning solutions from initial data exploration through production implementation
- Experience with software development practices, including Git, testing, logging, and code reviews
- Master's degree in a related quantitative field
- Experience in media, entertainment, streaming, or digital content platforms
- Experience building classifiers using content metadata, catalog data, or high-dimensional datasets
- Experience with NLP techniques such as TF-IDF, text embeddings, and semantic representations
- Experience with workflow orchestration tools such as Airflow or Luigi
- Experience with AWS or similar cloud environments
- Experience with search, recommendation systems, or content categorization
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The Company
What We Do
Often, the biggest barrier between setting business objectives and achieving them is talent. Finding technically qualified people when you need them is hard enough. Finding technically qualified people who are the best fit for your organization is tougher. You need a staffing partner with the right expertise who can find the right talent in the right time frame, because your project can’t wait. That’s where we come in. aKube Inc is committed to leveraging its corporate values and operating model to achieve the highest level of performance and respect within the industry. We have developed a highly efficient delivery model for supporting a wide array of clients with expertise in supporting high-volume contingent worker programs.
