**1. Executive Summary:**
The aim is to hire a proficient Senior Data Scientist for TechInnovate, capable of driving data-informed decision-making and leading complex analytical projects. Expert inputs have underlined the necessity for a robust evaluation framework assessing technical capability, practical experience, and soft skills. This synthesis provides a comprehensive approach to ensure the hiring of an experienced candidate who can significantly contribute to the company’s AI-driven solutions.

**2. Consolidated Ideas:**

**Key Responsibilities and Skills:**
- Design, develop, and implement advanced machine learning and NLP models.
- Collaborate with cross-functional teams, emphasizing communication and visualization.
- Maintain scalable data pipelines and ETL processes leveraging big data technologies like Hadoop and Spark.
- Mentor junior data scientists, and continuously stay abreast of machine learning and AI advancements.
- Proficiency in Python, R, SQL, and machine learning libraries like Scikit-learn, TensorFlow, and PyTorch.
- Experience with cloud platforms (AWS, GCP, Azure) and adherence to software engineering best practices (version control, code review, CI/CD).

**Technical and Evaluation Details:**
- Comprehensive knowledge of agile development methodologies.
- Design practical coding assignments and real-world problem-solving scenarios.
- Emphasis on candidates' contribution to open-source projects or research publications.
- Proficiency in data visualization tools such as Tableau or PowerBI.
- Evaluate portfolio presentations, peer code reviews, and technical interviews.
- Ensure familiarity with big data technologies and cloud infrastructure.

**3. Implementation Plan:**

**Pre-Hiring Assessments:**
- Develop technical assessments simulating real-world projects involving machine learning, NLP, data pipelines, and visualization using platforms like GitHub, HackerRank, or LeetCode.
- Conduct portfolio reviews of previous projects, research publications, and open-source contributions.

**Interview Process:**
- Structure interviews to cover:
   * Deep technical queries on ML, NLP, cloud platforms, and big data processing.
   * Practical coding tasks on version control, ETL pipelines, and CI/CD integration.
   * Soft skill assessments focused on communication, mentorship, and collaborative abilities.
   * Mock presentations explaining complex data insights to non-technical stakeholders.

**Onboarding and Training:**
- Implement mentoring programs for continuous skill enhancement.
- Regular workshops on new advancements in AI, machine learning, and big data technologies.

**4. Risk Mitigation:**

**Technical Gaps:**
- Mitigate by comprehensive and multi-step technical evaluations ensuring a balance between theoretical knowledge and practical expertise.

**Communication Barriers:**
- Conduct internal workshops aimed at enhancing the communication skills of technical experts to effectively present to non-technical stakeholders.

**Agile Misalignment:**
- Provide Agile training workshops and ensure experienced Scrum Masters guide the onboarding process.

**Version Control and Code Review Bottlenecks:**
- Streamline version control systems and establish standardized code review protocols to avoid delays and ensure efficient workflow.

**5. Success Metrics:**
- **Technical Competency:** Evaluate through coding assessments, portfolio reviews, and problem-solving exercises.
- **Collaboration and Communication:** Measure through feedback from cross-functional teams and mock presentations.
- **Project Efficiency:** Track through the implementation of data pipelines and model deployment success rates.
- **Mentorship Impact:** Assess by the growth and development of junior data scientists.
- **Innovation and Contributions:** Monitor the number of open-source contributions and research publications.

**6. Next Steps:**

**Immediate Actions:**
- Design and finalize the multi-tiered technical assessment framework.
- Collaborate with hiring teams to integrate soft skill evaluations into the interview process.
- Set up a dashboard for tracking candidate evaluations using tools like Tableau or PowerBI.

**Short-Term Actions:**
- Conduct initial candidate evaluations and shortlist potential hires based on consolidated criteria.
- Implement onboarding procedures including Agile training and mentorship programs.

**Long-Term Actions:**
- Establish continuous learning and development programs to keep the team updated with the industry trends.
- Regularly review and adapt the hiring and training processes based on feedback and success metrics to ensure the ongoing effectiveness of the approach.

By adhering to these comprehensive methods, TechInnovate can ensure the successful hiring of a Senior Data Scientist who will significantly contribute to the organization’s data science initiatives and AI-driven solutions.