Careers at DecisionTree

Key Responsibilities

End to End Delivery

• Own the complete lifecycle of Business Intelligence, DWH, and Data Analytics projects—from requirements gathering through development and deployment.
• Ensure delivery is on time, within budget, and of the highest quality.
• Act as the primary escalation point for project risks, challenges, and stakeholder concerns.

Project Quality & Governance

• Define and implement best practices, standards, and quality frameworks across BI, DWH, and Data Science initiatives.
• Guarantee that all solutions are scalable, secure, governance-compliant, and aligned with organizational policies.
• Drive continuous process improvement, governance maturity, and innovation in delivery methodologies.

Leadership & Mentoring

• Lead, mentor, and inspire cross-functional teams including data engineers, BI developers, data science professionals, and project managers.
• Provide technical guidance across BI, DWH, and Data Science domains.
• Foster a culture of accountability, collaboration, and delivery excellence.

Business & Stakeholder Management

• Partner with business leaders to understand needs and translate them into actionable BI, DWH, and DS solutions.
• Drive stakeholder alignment and ensure projects deliver measurable business outcomes.
• Present insights, project outcomes, and recommendations to senior leadership in a clear and business-oriented narrative.

Technology & Innovation

• Guide the adoption of modern BI platforms, cloud-based data warehousing solutions (e.g. Snowflake, Azure Synapse, Redshift, BigQuery), and data science models.
• Evaluate emerging technologies and DS methodologies to enhance efficiency, scalability, and analytical capabilities.

Essential Skills

• Education - Bachelor’s or Master’s in Computer Science, Data Management, Information Systems, or a related field.
• Experience - 20+ years overall, with at least 15 years in leading Data Warehousing, BI, and Analytics delivery.
• Expertise -Proven end-to-end delivery of large-scale BI, DWH, Data Analytics, and preferably DS projects.
• Governance & Quality - Deep understanding of project governance, quality frameworks, and compliance protocols.
• Technical Depth -Strong data modeling, ETL/ELT, BI platforms (Power BI, Tableau, Qlik), and familiarity with cloud ecosystems; fluency in DS tools, modeling, and predictive analytics.
• Leadership & Influence - Expert in team leadership, mentoring, stakeholder management, and executive communication.
• Innovation-Oriented: Proactive in adopting modern BI, Cloud DWH, and DS technologies and cultivating self-service analytics across teams.

Core Competencies Required

• Communication
• Adaptability
• Planning & Execution
• Collaboration
• Continual Learning

Key Responsibilities

• Design and implement advanced AI/ML models for business use cases, including but not limited to marketing mix modeling (MMM), churn prediction, propensity scoring, demand forecasting, and revenue/sales forecasting.
• Collaborate with cross-functional stakeholders — product managers, data engineers, domain experts — to translate business goals into AI/ML solutions.
• Leverage Python and modern ML/DL libraries (e.g., scikit-learn, PyTorch, statsmodels, langgraph) to process structured/unstructured data, build features, and develop scalable models.
• Lead end-to-end ML pipelines, from data ingestion and feature engineering to model training, evaluation, optimization, and deployment (including MLOps best practices).
• Conduct in-depth exploratory data analysis (EDA) and statistical investigations to extract actionable insights, validate hypotheses, and guide model development.
• Stay abreast of emerging ideas in AI/ML, and actively integrate ideas from research.
• Work across the spectrum of traditional machine learning and modern AI architectures, including the development of deep neural networks.
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Essential Skills

• Educational Background - Bachelor's, Master's, or Ph.D. in a quantitative discipline such as Statistics, Mathematics, Computer Science, Data Science, AIMLor related fields.
• Hands-on Experience - 2–4 years of experience in building and deploying statistical & ML/AI models. Preferred exposure includes use cases such as MMM, churn prediction, demand/sales forecasting, and transformer-based architectures.
• Programming Expertise - Strong command of Python for data science and machine learning workflows, including experience with packages like NumPy, Pandas, Scikit-learn, Optuna and deep learning frameworks such as PyTorch.
• Modular & Standards-Compliant Coding - Proficient in writing clean, modular, and maintainable code following PEP8 standards and software engineering best practices (including version control, documentation, and reusable components).
• ML Engineering Fundamentals - Proficient in both traditional machine learning algorithms and neural network architectures (e.g., feedforward, convolutional, transformer-based), with a solid grasp of data preprocessing, feature engineering, and model evaluation techniques.
• End-to-End Pipeline Exposure - Experience working across the entire model lifecycle — from data ingestion and EDA to training, tuning, deployment, and monitoring.
• Business Communication - Ability to translate complex analytical results into clear, actionable insights for both technical and non-technical stakeholders.
  ‍

Core Competencies Required

• Effective Communication - Articulation of technical concepts, results, and recommendations across diverse teams.
• Mathematical Thinking & Creativity - Ability to approach problems with analytical rigor and devise innovative algorithmic solutions grounded in mathematical principles.
• Passion for New Ideas & Research - Curiosity and enthusiasm for exploring emerging techniques, experimenting with novel approaches, and contributing to innovation in AI/ML.
• Execution-Oriented Planning - Ability to structure complex problems and drive solutions with a balance of speed and accuracy.
• Cross-Functional Collaboration - Partnering with product, engineering, marketing, sales and leadership teams to align AI solutions with business goals.

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Key Responsibilities

• Design and implement advanced AI/ML models for business use cases, including but not limited to marketing mix modeling (MMM), churn prediction, propensity scoring, demand forecasting, and revenue/sales forecasting.
• Collaborate with cross-functional stakeholders — product managers, data engineers, domain experts — to translate business goals into AI/ML solutions.
• Leverage Python and modern ML/DL libraries (e.g., scikit-learn, PyTorch, statsmodels, langgraph) to process structured/unstructured data, build features, and develop scalable models.
• Lead end-to-end ML pipelines, from data ingestion and feature engineering to model training, evaluation, optimization, and deployment (including MLOps best practices).
• Conduct in-depth exploratory data analysis (EDA) and statistical investigations to extract actionable insights, validate hypotheses, and guide model development.
• Stay abreast of emerging ideas in AI/ML, and actively integrate ideas from research.
• Work across the spectrum of traditional machine learning and modern AI architectures, including the development of deep neural networks.
  ‍

Essential Skills

• Educational Background - Bachelor's, Master's, or Ph.D. in a quantitative discipline such as Statistics, Mathematics, Computer Science, Data Science, AIMLor related fields.
• Hands-on Experience - 4–6 years of experience in building and deploying statistical & ML/AI models. Preferred exposure includes use cases such as MMM, churn prediction, demand/sales forecasting, and transformer-based architectures.
• Programming Expertise - Strong command of Python for data science and machine learning workflows, including experience with packages like NumPy, Pandas, Scikit-learn, Optuna and deep learning frameworks such as PyTorch.
• Modular & Standards-Compliant Coding - Proficient in writing clean, modular, and maintainable code following PEP8 standards and software engineering best practices (including version control, documentation, and reusable components).
• ML Engineering Fundamentals - Proficient in both traditional machine learning algorithms and neural network architectures (e.g., feedforward, convolutional, transformer-based), with a solid grasp of data preprocessing, feature engineering, and model evaluation techniques.
• End-to-End Pipeline Exposure - Experience working across the entire model lifecycle — from data ingestion and EDA to training, tuning, deployment, and monitoring.
• Business Communication - Ability to translate complex analytical results into clear, actionable insights for both technical and non-technical stakeholders.
  ‍

Core Competencies Required

• Effective Communication - Articulation of technical concepts, results, and recommendations across diverse teams.
• Mathematical Thinking & Creativity - Ability to approach problems with analytical rigor and devise innovative algorithmic solutions grounded in mathematical principles.
• Passion for New Ideas & Research - Curiosity and enthusiasm for exploring emerging techniques, experimenting with novel approaches, and contributing to innovation in AI/ML.
• Execution-Oriented Planning - Ability to structure complex problems and drive solutions with a balance of speed and accuracy.
• Cross-Functional Collaboration - Partnering with product, engineering, marketing, sales and leadership teams to align AI solutions with business goals.

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Job Role

An Exceptional Senior Data Analyst responsible for the technical implementation of current and future solutions in the Business Intelligence domain. You should be proficient with the design of database architectures, data warehouses, data migrations, data marts, business intelligence packages, and implementing user reporting solutions based upon business requirements with a strong exposure to SQL.
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Key Responsibilities

• Design, develop, and maintain the data warehouse and analytics architecture to meet business analysis and reporting needs, where the scale and reliability of a modern distributed system is at the heart.
• Primarily responsible for the successful delivery of client engagements and initiatives.
• Provide subject matter expert input and advisory role input to client engagements as required – strategy and solution advisory, technical resource management, client workshop facilitation and overall engagement management.
• Write stored procedures, functions, views, triggers for various database systems.
• Review and evaluate existing stored procedures for performance improvements
• Be a team player and work directly with other development teams and analysts to support our prototype solutions, maintaining flexibility and accountability to keep project progressing
• Always be learning and looking to expand the breadth of your expertise outside your comfort zone because the business is evolving and you need to stay current.
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Qualification & Required Skills

• A MS/BS degree in Computer Science or related technical field preferred. Additional education in business or statistics a plus
• 3+ years of work experience in using SQL in Analytics Deployment.
• Domain knowledge of digital marketing is preferred.
• Must excel at adapting to unpredictable scenarios, thinking logically and analytically, and learning quickly
• Strong foundation in SQL, particularly its usage in Business Intelligence and Analytics solutions:
• Strong scripting skills to perform data/file manipulation
• Strong background in Data Warehousing and Business Analytics environment
• Ability to read and interpret data schemas,
• Experience in working with various data sources: Web Services, application API, XML, Relational Databases, Document Oriented NoSQL databases, etc.
• Experience working with AWS cloud-based services: S3, EC2, RDS, Redshift, Snowflake
• Excellent verbal and written communication skills.
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Core Competencies Required

• Communication
• Adaptability
• Planning & Execution
• Collaboration
• Continual Learning