Senior Consultant, Data Science & AI

TTEC Digital•Austin, TX

1d•$120,000 - $134,996•Remote

About The Position

At TTEC Digital, we coach clients to ensure their employees feel valued, and fully supported, because an amazing customer experience is an employee first process. Our vision is the same, a place where employees know they can thrive. The position of Senior Consultant - Data Science & AI is within the TTEC Digital Analytics team. The Analytics group is responsible for Data Science, Advanced Analytics, Generative AI, and Engineering projects that include the design and validation of predictive models, building scalable machine learning pipelines, engineering autonomous agentic workflows, and leveraging enterprise Gen AI (LLMs, RAG architectures) to convert raw, unstructured client data into actionable insights that drive strategic business decision-making. Our Data Science & AI Consultants work directly with external clients, Data Engineers, Project Leads, and Managers on implementation, migration, and advanced analytics deployment projects.

Requirements

Post-Secondary Degree (or Diploma) related to Data Science, Statistics, Computer Science, Economics, Business Analytics, or an IT-related field.
5–8 years of total experience in Data Science, Advanced Analytics, or Management Consulting.
3+ years of experience in an external client-facing consulting or professional services capacity.
3+ years of application, model design, and deployment experience natively within a cloud environment (GCP preferred).
1–2+ years of hands-on experience architecting and deploying Generative AI systems (e.g., RAG pipelines, fine-tuned LLMs) and Agentic AI workflows (e.g., autonomous agents, tool-use integration).
Demonstrated experience delivering both traditional predictive models and modern AI solutions to enterprise stakeholders.
Python Stack: pandas, numpy, scikit-learn, XGBoost, PyTorch/TensorFlow, Transformers
Generative & Agentic AI Frameworks: LangChain, LlamaIndex, AutoGen/CrewAI, Prompt Engineering, RAG architectures, Multi-Agent Orchestration, Function Calling & Tool Integration
Vector Databases & Search: BigQuery Vector Search, Google Cloud Vector Search, Pinecone, Chroma, or FAISS
SQL: Advanced query optimization for large-scale data warehouses
Google Cloud Platform (Core focus): Gemini Enterprise, Vertex AI Agent Builder, BigQuery ML, Model Garden (Model development, fine-tuning, and deployment)
Data Engineering & Orchestration: BigQuery, Dataflow, and Cloud Storage for data extraction, pipeline management, and indexing
BI & Data Visualization Tools: Looker / Looker Studio, Tableau, or Power BI
MarTech/AdTech Ecosystems
Agile/Scrum Methodologies
CI/CD & MLOps/LLMOps: Version control, automated deployment pipelines, LLM evaluation, and model monitoring tools
Personal: Strong interpersonal skills, high energy and enthusiasm, integrity, and honesty; flexible, results-oriented, resourceful, structured problem-solving ability, deal effectively with difficult client situations, ability to prioritize.
Leadership: Ability to gain credibility, motivate, and provide leadership; work with a diverse external customer base; maintain a positive attitude. Provide technical support and guidance to more junior team members, particularly for challenging modeling or analytical assignments.
Operations: Commercial acumen and ability to manage multiple projects simultaneously. Perform tasks in a client-friendly manner while utilizing time and resources efficiently, proactively managing project scope.
Technical: Ability to understand, translate, and communicate highly complex technical and mathematical concepts to non-technical business stakeholders.

Nice To Haves

Google Cloud Certified Professional Data Engineer, Professional Machine Learning Engineer, or equivalent Generative AI/Cloud Certifications (Highly Preferred).

Responsibilities

Develop Machine Learning Models: End-to-end development, training, and deployment of production-grade predictive and prescriptive models (e.g., propensity modeling, customer segmentation, demand forecasting).
Design & Deploy Generative AI Solutions: Architect, fine-tune, and evaluate enterprise Gen AI systems—including Retrieval-Augmented Generation (RAG) pipelines, custom LLM applications, and prompt engineering frameworks—to extract insights from unstructured data.
Architect Agentic AI Systems: Design and deploy autonomous AI agents and multi-agent workflows capable of multi-step reasoning, tool utilization, and task automation to solve complex client business challenges.
Deliver Advanced Analytics: Help clients maximize the efficiency of their strategic initiatives by building advanced analytics models that measure campaign performance, predict customer behavior, and maximize long-term ROI.
Data Storytelling & Visualization: Synthesize complex algorithmic outcomes and machine learning metrics into compelling, executive-ready presentations and dashboards that clearly articulate business value and ROI.
Develop and Maintain Analytics Workflows: Analyze raw client data and build optimized analytics datasets within cloud environments to support modeling and reporting.
Lead workshops with external clients to uncover business objectives, map out data landscapes, and translate vague business problems into structured data science methodologies.
Create solutions and design documentation for client data science architectures and ML pipelines.
Work on projects independently as well as being part of a large team across multiple client engagements.
Crosstrain Junior Data Scientists/Analysts or other team members with your area of expertise, providing technical leadership and code reviews.
Further develop skills both on the job and through formal learning channels to stay ahead of AI/ML trends.
Assist in pre-sales activities by scoping new client opportunities and providing accurate work/effort estimates.