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
- . 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)
Key Skills:
- . Machine Learning
- . Natural Language Processing
- . Recommendation Systems
- . Time Series Forecasting
- . Google Cloud Platform BigQuery Cloud Dataflow Cloud Pub/Sub Dataprep Cloud Dataproc Vertex AI
- . Kubernetes
- . Docker
- . Python
- . MLOps
- . Data Analysis and Visualization
- . Data Ingestion and Wrangling
What We Offer:
- . Opportunity to work on cutting-edge ML projects with real-world impact
- . Collaborative and innovative work environment
- . Continuous learning and professional development opportunities
- . Competitive salary and benefits package
- . Flexible work arrangements
Skills: - Natural Language Processing
- Telecommunications
- MySQL
