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.
Key Responsibilities:Develop and implement machine learning models for NLP sentiment analysis and scoringCreate and optimize app recommendation systems using advanced ML techniquesBuild and maintain sales forecasting models to drive business insightsDesign and implement MLOps pipelines for model training, deployment, and monitoringContainerize ML applications and deploy them on Kubernetes clustersCollaborate with data engineers to design and implement data ingestion and wrangling pipelines using Google Cloud servicesUtilize BigQuery for large-scale data analysis and feature engineeringContinuously improve model performance and operational efficiency

Master's degree in Computer Science, Data Science, or a related field
3+ years of experience in machine learning and data science rolesStrong 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 forecastingSolid understanding of MLOps principles and practicesProficiency 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 analysisExperience 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 PyTorchKnowledge of other cloud platforms (e.g., Azure, AWS) is a plusFamiliarity 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 practicesExperience with data visualization tools (e.g., Data Studio, Looker)Key Skills:Machine LearningNatural Language ProcessingRecommendation SystemsTime Series ForecastingGoogle Cloud Platform BigQuery Cloud Dataflow Cloud Pub/Sub Dataprep Cloud Dataproc Vertex AIKubernetesDockerPythonMLOpsData Analysis and VisualizationData Ingestion and WranglingSkills:

• Natural Language Processing
• Telecommunications
• MySQL.