HM Note: This hybrid contract role is five (5) days in office. Candidate resumes must include first and last name, email and telephone contact information.

Description

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

Skills

Experience and Skill Set Requirements

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 (TensorFlow, PyTorch, or Keras) is a plus

Data Visualization & Reporting

• Expertise with visualization libraries (matplotlib, seaborn, plotly, or equivalent)
• Experience building interactive dashboards (Tableau, Power BI, Dash, or Streamlit)
• Ability to design clear, impactful data narratives and reports

Data Infrastructure & Tools

• Experience with cloud-based data services (e.g., AWS S3, Redshift, Azure Data Lake, GCP BigQuery)
• Experience working with big data frameworks such as Apache Spark and Hadoop for large-scale data processing.
• Familiarity with data pipeline and workflow tools
• Experience with API integration and data automation scripts (Selenium, Python, etc)
• Solid grounding in probability, statistics, and linear algebra
• Understanding of hypothesis testing, confidence intervals, and sampling methods

Soft Skills- 20%

• Strong communication skills; both written and verbal
• Ability to develop and present new ideas and conceptualize new approaches and solutions
• Excellent interpersonal relations and demonstrated ability to work with others effectively in teams
• Demonstrated ability to work with functional and technical teams Demonstrated ability to participate in a large team and work closely with other individual team members
• Proven analytical skills and systematic problem solving
• Strong ability to work under pressure, work with aggressive timelines, and be adaptive to change
• Displays problem-solving and analytical skills, using them to resolve technical problems

Public sector Experience- 5%

• OPS(or other government) standards and processes

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.