What you will do:

• Design, develop, and research best-in-class machine learning models and systems.
• Deploy new models and systems to production while ensuring compatibility with the overall system.
• Build, study, transform, and convert data science prototypes into production code.
• Discover, build and curate appropriate datasets.
• Work in multi-functional agile teams to continuously experiment, iterate and deliver new product objectives.
• Perform statistical analysis for insights and use results to improve models.
• Train and retrain ML systems and models as needed
• Monitor and identify model drifts in production.
• Analyze the business use cases against the ML algorithms to determine their probability of meeting business goals.
• Stay on top of brand-new ML models to apply to real-world use cases.

Basic Qualifications:

• Bachelor’s Degree (BA, BSc) and or Graduate level (MA, MSc, MFA) in computer science, math, statistics, or a related field preferred
• 5+ years of programming experience with Python and data handling using modules like NumPy and Pandas
• 2+ years of strong working experience in ML algorithms & models research, design, development, model training, and data analysis
• Strong working experience with high dimensional data, applying techniques like Principal Component Analysis (PCA) and clustering for exploring and visualizing data
• Experience in building machine learning models like Regression, Decision Trees, Ensemble techniques, Support Vector Machines, etc
• Strong working experience in using python packages like Plotly, Matplotlib, etc
• Ability to research and develop solutions with different NLP-based libraries like Gensim, NLTK, Spacy, etc
• Good working knowledge of different models and methods for NLP using pre-trained models like BERT, FastText, etc
• Feature engineering experience passionate about batch and real-time data pipelines development using Spark, Python, or Scala
• Working experience in SQL
• Complete software development lifecycle experience including design, documentation, implementation, testing, and deployments
• Knowledge of ML model deployments (CI-CD) in cloud infrastructure