Data Scientist

Position Description

We are seeking a talented and experienced Data Scientist / MLOps Engineer to join our team. In this role, you will be responsible for

developing and operationalizing machine learning models, with a focus on NLP sentiment analysis, scoring, app recommendations, and sales

forecasting. You will work closely with cross-functional teams to implement these solutions using Google Cloud services, Kubernetes, and

containerization technologies.

Key Responsibilities:

• Develop and implement machine learning models for NLP sentiment analysis and scoring
• Create and optimize app recommendation systems using advanced ML techniques
• Build and maintain sales forecasting models to drive business insights
• Design and implement MLOps pipelines for model training, deployment, and monitoring
• Containerize ML applications and deploy them on Kubernetes clusters
• Collaborate with data engineers to design and implement data ingestion and wrangling pipelines using Google Cloud services
• Utilize BigQuery for large-scale data analysis and feature engineering
• Continuously improve model performance and operational efficiency

Required Qualifications:

• Master's degree in Computer Science, Data Science, or a related field
• 4+ years of experience in machine learning and data science roles
• Strong proficiency in Python and data science libraries (e.g., NumPy, Pandas, Scikit-learn)
• Expertise in NLP techniques and frameworks (e.g., NLTK, spaCy, Transformers)
• Experience with recommendation systems and time series forecasting
• Solid understanding of MLOps principles and practices
• Proficiency in Google Cloud Platform services, especially: AI/ML offerings (e.g., Vertex AI, AutoML) Data ingestion services (e.g., Cloud Dataflow, Cloud Pub/Sub) Data processing services (e.g., Dataprep, Cloud Dataproc) BigQuery for large-scale data analysis
• Experience with containerization (Docker) and orchestration (Kubernetes)
• Familiarity with CI/CD pipelines and version control systems (e.g., Git)