Data Scientist

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.
: . 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: . Masters degree in Computer Science, Data Science, or a related field . 3+ 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) Preferred Qualifications: . Experience with TensorFlow and/or PyTorch . Knowledge of other cloud platforms (e.g., Azure, AWS) is a plus . Familiarity with big data technologies (e.g., Spark, Hadoop) . Experience with ML model serving frameworks (e.g., TensorFlow Serving, KFServing) . Understanding of data privacy and security best practices . Experience with data visualization tools (e.g., Data Studio, Looker)