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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
Your future duties and responsibilities
Required qualifications to be successful in this role
Together, as owners, let’s turn meaningful insights into action.
Life at CGI is rooted in ownership, teamwork, respect and belonging. Here, you’ll reach your full potential because…
You are invited to be an owner from day 1 as we work together to bring our Dream to life. That’s why we call ourselves CGI Partners rather than employees. We benefit from our collective success and actively shape our company’s strategy and direction.
Your work creates value. You’ll develop innovative solutions and build relationships with teammates and clients while accessing global capabilities to scale your ideas, embrace new opportunities, and benefit from expansive industry and technology expertise.
You’ll shape your career by joining a company built to grow and last. You’ll be supported by leaders who care about your health and well-being and provide you with opportunities to deepen your skills and broaden your horizons.
Come join our team—one of the largest IT and business consulting services firms in the world.
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