Job Specifications
Please send your resume at mounika.karnati@amyantek.com if you are interested in this 05 month contract with Ontario Public Service(Ministry of Public and Business Service Delivery and Procurement) with a possibility of extension, If you are not interested, please feel free to pass it in your network for anyone looking for work.
Job Title: RQ09863 - Intermediate Data Science Developer
Working Status: 4 Days a week till 4th Jan 2026 and then Full time onsite
Location: 222 Jarvis Street, Toronto, Ontario
Hours per day: 7.25
Must Have:
2–5 years of professional experience in data science, data analytics, or a related quantitative field (e.g., data engineering, machine learning, or business intelligence) or equivalent.
Proven experience in data analysis, visualization, and statistical modeling for real-world business or research problems.
Demonstrated ability to clean, transform, and manage large datasets using Python, R, or SQL.
Programming & Data Handling
Python (pandas, NumPy, scikit-learn, statsmodels, matplotlib, seaborn)
SQL (complex queries, joins, aggregations, optimization)
Data preprocessing (feature engineering, missing data handling, outlier detection)
Experience working with big data frameworks such as Apache Spark and Hadoop for large-scale data processing.
Responsibilities
Participate in product teams to analyze systems requirements, architect, design, code and implement cloud-based data and analytics products that conform to standards.
Design, create, and maintain cloud-based data lake and lakehouse structures, automated data pipelines, analytics models, and visualizations (dashboards and reports).
Liaises with cluster IT colleagues to implement products, conduct reviews, resolve operational problems, and support business partners in effective use of cloud-based data and analytics products.
Analyses complex technical issues, identifies alternatives and recommends solutions.
Prepare and conduct knowledge transfer
General Skills
Experience in multiple cloud base data and analytics platforms and coding/programming/scripting tools to create, maintain, support and operate cloud-based data and analytics products.
Experience with designing, creating and maintaining cloud-based data lake and lakehouse structures, automated data pipelines, analytics models, and visualizations (dashboards and reporting) in real world implementations
Experience in assessing client information technology needs and objectives
Experience in problem-solving to resolve complex, multi-component failures
Experience in preparing knowledge transfer documentation and conducting knowledge transfer
A team player with a track record for meeting deadlines
Desirable Skills
Written and oral communication skills to participate in team meetings, write/edit systems documentation, prepare and present written reports on findings/alternate solutions, develop guidelines / best practices Interpersonal skills to explain and discuss advantages and disadvantages of various approaches
Experience in conducting knowledge transfer sessions and building documentation for technical staff related to architecting, designing, and implementing end to end data and analytics products
Technology Stack Azure Storage, Azure Data Lake, Azure Databricks Lakehouse, and Azure Synapse Python, SQL, Azure Databricks and Azure Data Factory Power BI
Rated Criteria
Experience - 40 %
2–5 years of professional experience in data science, data analytics, or a related quantitative field (e.g., data engineering, machine learning, or business intelligence) or equivalent.
Proven experience in data analysis, visualization, and statistical modeling for real-world business or research problems.
Demonstrated ability to clean, transform, and manage large datasets using Python, R, or SQL.
Hands-on experience building and deploying predictive models or machine learning solutions in production or business environments.
Experience with data storytelling and communicating analytical insights to non-technical stakeholders.
Exposure to cloud environments (AWS, Azure, or GCP) and version control tools (e.g., Git).
Experience working in collaborative, cross-functional teams, ideally within Agile or iterative project structures.
Knowledge of ETL pipelines, APIs, or automated data workflows is an asset.
Previous work with dashboarding tools (Power BI, Tableau, or Looker) is preferred.
Technical Skills - 35%
Programming & Data Handling
Python (pandas, NumPy, scikit-learn, statsmodels, matplotlib, seaborn)
SQL (complex queries, joins, aggregations, optimization)
Data preprocessing (feature engineering, missing data handling, outlier detection)
Machine Learning & Statistical Modeling
Proficiency in supervised and unsupervised learning techniques (regression, classification, clustering, dimensionality reduction)
Understanding of model evaluation metrics and validation techniques (cross-validation, A/B testing, ROC-AUC, confusion matrix)
Basic understanding of deep learning frameworks