About ProArch:
At ProArch, we partner with businesses around the world to turn big ideas into better outcomes through IT services that span cybersecurity, cloud, data, AI, and app development. We’re 400+ team members strong across 3 countries (we call ourselves ProArchians)—and here’s what connects us all:
- A love for solving real business problems
- A belief in doing what’s right
What’s it like to work here?
- You’ll keep growing. You’ll work alongside domain experts who love to share what they know.
- You’ll be supported, heard, and trusted to make an impact.
- You’ll take on projects that touch industries, communities, and lives.
- You’ll have the time to focus on what matters most in your life outside of work.
At ProArch, you’ll be part of teams that design and deliver technology solutions solving real business challenges for our clients. With services spanning AI, Data, Application Development, Cybersecurity, Cloud & Infrastructure, and Industry Solutions, your work may involve building intelligent applications, securing business‑critical systems, or supporting cloud migrations and infrastructure modernization.
Every role here contributes to shaping outcomes for global clients and driving meaningful impact. You’ll collaborate with experts across data, AI, engineering, cloud, cybersecurity, and infrastructure—solving complex problems with creativity, precision, and purpose. You’ll join a culture rooted in technology, curiosity, and continuous learning. A place where we move fast, trust you to make an impact, encourage innovation, and support your growth.
Job Description:
We are seeking a Senior Associate Data Scientist to independently own client-facing data science and machine learning workstreams - from business discovery and analytical design through deployment and performance monitoring.
The role combines hands-on statistical and machine learning development with solution design, stakeholder engagement, and technical mentorship. You will collaborate with data engineers, ML engineers, architects, product teams, and business stakeholders to deliver reliable solutions with measurable business outcomes.
This is a hands-on role for someone who can connect business problems with technically sound, production-ready data science solutions.
Key Responsibilities:
Data Science and Machine Learning:
- Design, develop, evaluate, and deploy machine learning and advanced analytics solutions for complex business problems.
- Apply appropriate methods across forecasting, classification, regression, clustering, optimization, and statistical analysis.
- Establish relevant baselines, evaluation metrics, and validation approaches based on the business objective.
- Perform exploratory analysis, feature engineering, model selection, error analysis, and performance optimization.
- Work with structured, semi-structured, and unstructured datasets.
- Clearly distinguish between correlation, prediction, and causation when interpreting analytical results.
- Translate model outputs into practical recommendations and measurable business actions.
Production ML and MLOps:
- Build reproducible training, validation, and inference pipelines.
- Collaborate with engineering teams to deploy batch or real-time machine learning solutions.
- Apply software engineering practices including Git, code reviews, modular development, testing, and documentation.
- Implement experiment tracking, model versioning, deployment automation, and appropriate CI/CD practices.
- Define and implement model monitoring, data-quality checks, drift detection, retraining, and performance-management processes.
- Consider reliability, scalability, latency, cost, security, privacy, and maintainability during solution design.
- Support model governance, explainability, auditability, and responsible AI requirements.
Cloud Data and Analytics Platforms:
- Develop data science and analytics solutions using Azure or comparable cloud platforms.
- Work with one or more platforms such as Azure Databricks, Microsoft Fabric, Azure Machine Learning, Azure Synapse Analytics, or equivalent technologies.
- Collaborate with data engineers to define data requirements and support reliable ingestion, transformation, and feature-generation pipelines.
- Work with large enterprise datasets and implement suitable data-quality and validation controls.
- Contribute to scalable analytical and machine learning architectures without unnecessarily increasing solution complexity.
Business Analysis and Consulting:
- Participate in discovery workshops, requirements discussions, and solution-design sessions.
- Translate broad business challenges into clearly defined analytical problems.
- Define success metrics, assumptions, constraints, risks, and acceptance criteria before model development.
- Evaluate the feasibility and expected value of proposed data science solutions.
- Challenge the use of machine learning where a simpler analytical or rules-based solution would be more appropriate.
- Communicate analytical findings, limitations, and recommendations clearly to technical and non-technical stakeholders.
- Present project outcomes and recommendations to client and internal leadership teams.
Delivery and Collaboration:
- Independently own defined data science workstreams from discovery through deployment.
- Collaborate with data engineers, software engineers, architects, analysts, product teams, and subject-matter experts.
- Provide realistic estimates and proactively communicate dependencies, risks, and delivery concerns.
- Review analytical approaches, model implementations, and technical outputs for quality and consistency.
- Maintain clear documentation covering data, assumptions, experiments, models, limitations, and operational processes.
- Contribute to successful project delivery while balancing technical quality, business value, and timelines.
Mentorship and Continuous Improvement:
- Mentor junior data scientists and analysts through technical guidance, code reviews, and knowledge sharing.
- Promote reproducible experimentation, sound statistical practices, and responsible use of AI.
- Identify opportunities to improve existing models, processes, reusable assets, and delivery practices.
- Stay current with relevant developments in data science, machine learning, MLOps, and Generative AI.
- Contribute to internal capability development, technical discussions, and innovation initiatives.
Requirements
Required Qualifications
- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Artificial Intelligence, or another relevant discipline - or equivalent practical experience.
- Five or more years of relevant experience in data science, machine learning, advanced analytics, or a closely related field.
- Proven ability to independently deliver at least one data science or machine learning solution from problem definition through deployment or operational use.
- Advanced proficiency in Python and SQL.
- Strong knowledge of statistical analysis, feature engineering, experimentation, model evaluation, and error analysis.
- Practical experience in multiple relevant areas such as forecasting, regression, classification, clustering, optimization, or anomaly detection. Expertise in every area is not required.
- Hands-on experience with commonly used machine learning libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, or equivalent frameworks, depending on the use case.
- Experience working with large, complex, or imperfect datasets and implementing appropriate data-quality controls.
- Understanding of production ML practices, including model deployment, versioning, monitoring, and retraining.
- Experience using Git and following collaborative software-development practices.
- Ability to translate business requirements into measurable analytical objectives.
- Strong written and verbal communication skills, including the ability to explain technical concepts to non-technical stakeholders.
- Experience owning technical workstreams and supporting less-experienced team members.
- Experience flexibility. Candidates with a strong academic background and demonstrated technical ability may be considered with flexibility on the experience requirement.
Preferred Qualifications
- Experience delivering data science or analytics solutions in Microsoft Azure.
- Practical experience with Azure Databricks, Microsoft Fabric, Azure Machine Learning, Azure Synapse Analytics, or comparable platforms.
- Experience with MLflow or an equivalent experiment-tracking and model-management platform.
- Exposure to automated testing, containerization, APIs, CI/CD, and cloud deployment.
- Experience in consulting, professional services, or another customer-facing environment.
- Experience facilitating discovery workshops or presenting analytical recommendations to senior stakeholders.
- Knowledge of data governance, model risk management, responsible AI, privacy, and security practices.
- Experience developing or evaluating Generative AI, retrieval-augmented generation, or agentic AI solutions.
- Understanding of Generative AI evaluation considerations, including accuracy, hallucination, safety, latency, and cost.
- Experience working in domains such as forecasting, supply chain, customer analytics, financial services, healthcare, retail, or manufacturing.
Key Competencies
Technical
- Applied machine learning and statistical analysis.
- Model evaluation and experimentation.
- Python and SQL development.
- Feature engineering and data-quality validation.
- Cloud-based data science and analytics.
- Production ML and model lifecycle management.
- Analytical solution design.
- Responsible and explainable AI.
Business and Consulting
- Business-problem definition.
- Analytical thinking and structured problem-solving.
- Commercial and business awareness.
- Requirements discovery and solution design.
- Ability to connect analytical performance with business outcomes.
- Risk, feasibility, and value assessment.
Communication and Leadership
- Clear written and verbal communication.
- Data storytelling and visualization.
- Ability to communicate uncertainty and model limitations.
- Client-facing presentation and facilitation.
- Technical ownership and accountability.
- Constructive review and mentorship.
- Effective cross-functional collaboration.