Full Time | Canada, Ontario | $43.00/Hr | 2024-05-01 04:06:55 | LTIMINDTREE
Data scientists use statistical methods to collect and organize data. Problem-solving skills. Data scientists must devise solutions to the problems they encounter in data collection and cleaning and in developing statistical models and algorithms.
Job Descriptions
Data scientists examine which questions need answering and where to find the related data.
He must possess business acumen and analytical skills as well as the ability to mine, clean, and present data.
Must be hands-on coding capable in at least a core Language skill of Python
Expertise in working with Distributed DW and Cloud Services (like Snowflake, AWS etc.) via scripted pipelines for at least 2-3 years.
Solid grasp of database engineering and design Leveraged frameworks & orchestration as required for ETL pipeline Identify bottlenecks and bugs in the system and develop scalable solutions Unit Test & Document deliverables Capacity to successfully manage a pipeline of duties with minimal supervision.
Experience with AWS suite of Services/Resources
Experience in Agile methodologies (Kanban and SCRUM).
CICD Pipeline Orchestration. Experience with CI/CD tools (e.g., Jenkins or equivalent), version control (Git), orchestration/DAGs tools (AWS Step Functions, Airflow, Luigi, Kubeflow, or equivalent).
Building data pipelines (ETL) and using tools like Apache Spark
Programming Languages - Python, SQL, OOP - Object Oriented Programming
ML Frameworks/Open-Source Libraries - scikit-learn, TensorFlow, PyTorch, Keras and others • Experience with Data Science Lifecycle - Acquisition, data processing, building models, optimization, deployment and maintenance
Contribute to building advanced analytics, machine learning platform and tools to enable both prediction and optimization of models. Extend existing ML Platform and frameworks for scaling model training & deployment.
Experience working with machine learning models with respect to deployment, inference, tuning, and measurement required.
Experience with building data pipelines in getting the data required to build and evaluate ML models
Data movement technologies (ETL/ELT), Messaging/Streaming Technologies (AWS SQS, Kinesis/Kafka), Relational and NoSQL databases (DynamoDB, EKS, Graph database), API and in-memory technologies.
Strong knowledge of developing highly scalable distributed systems using Open-source technologies.
Must have the ability to deal with ambiguity and work in fast paced environment.
Must be familiar with applied data science methods, feature engineering and machine learning algorithms.
Must have Data wrangling experience with structured, semi-structure and unstructured data.
Must have excellent communication skills, both through written and verbal channels.
Must have excellent collaboration skills to work with multiple teams in the organization.
Must be able to understand and adapt to changing business priorities and technology advancements in Big data.