Responsibilities

Collaborate with practice leads and clients to grasp business objectives, industry dynamics, available data, and operational limitations.
Convert business requirements into feasible data science initiatives by assessing various technical strategies and articulating their pros and cons.
Engage with cross-functional teams to agree on analytical approaches, expected outputs, and project timelines.
Apply machine learning and data analysis techniques to enhance the efficiency and impact of marketing efforts.
Run A/B testing programs to assess and refine campaign outcomes, boosting user engagement and conversion metrics.
Develop and deploy production-level artificial intelligence systems using large language models, transformers, retrieval-augmented generation, agentic processes, and generative agents.
Refine prompt engineering, processing workflows, and data pipelines for optimal accuracy, speed, and cost.
Construct multi-stage, state-aware agent architectures that integrate external APIs and tools while enabling strong logical reasoning.
Operationalize generative AI models and pipelines through API, batch, or real-time streaming methods with emphasis on scalability and dependability.
Establish evaluation mechanisms to track model grounding, factual consistency, response time, and resource usage.
Enforce safeguards including defenses against prompt injection, content filtering, loop detection, and controlled tool access.
Partner with Product, Engineering, and Machine Learning Operations teams to deliver fully integrated, high-performance AI solutions.
Plan and oversee detailed project schedules, defining milestones, risk factors, responsible parties, and backup strategies.
Design and manage scalable data processing pipelines using SQL, Spark, and cloud-based big data platforms within client infrastructure.
Gather, preprocess, and unify large-scale datasets from diverse internal and external sources to meet business needs.
Create analytical tools that generate actionable insights in areas such as customer acquisition, operational efficiency, and performance tracking.
Conduct exploratory data analysis, data mining, and statistical modeling to reveal patterns and guide strategic planning.
Develop, validate, and calibrate predictive models using advanced machine learning frameworks and algorithms.
Record model findings clearly for client audiences and assist in deploying models within client systems.

Compensation

Competitive salary and performance-based incentives

Work Arrangement

Hybrid or remote options available

Team

Cross-functional collaboration with data scientists, engineers, and product teams

Qualifications

Advanced degree in data science, computer science, or related field.
Proven experience leading data science projects in marketing or customer analytics.
Strong proficiency in Python, SQL, and machine learning libraries.
Hands-on experience with LLMs, prompt engineering, and generative AI frameworks.
Familiarity with cloud platforms such as AWS, GCP, or Azure.
Experience deploying models in production environments using MLOps practices.
Excellent communication skills with ability to explain technical concepts to non-technical stakeholders.

Preferred Skills

Experience with retrieval-augmented generation and vector databases.
Background in A/B testing design and statistical inference.
Knowledge of marketing technology stacks and customer data platforms.
Track record of delivering AI solutions in regulated or enterprise environments.

Available for qualified candidates