India Job Openings

Tesseract Imaging
Associate Director of Data Science - XR
September 15, 2024
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Data Science Strategy and Vision
- Define the overall data science strategy, aligning it with the organization’s objectives and goals.
- Drive the adoption of advanced analytics, machine learning, and AI to solve complex business problems.
- Identify new opportunities to leverage data science for product innovation, customer insights, and operational efficiency.
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Team Leadership and Development
- Lead, mentor, and manage a growing team of data scientists, machine learning engineers, and data analysts.
- Foster a culture of innovation, collaboration, and continuous improvement within the data science team.
- Ensure professional development and skill growth by providing mentorship, setting clear goals, and conducting performance reviews.
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Data Science Solutions Development
- Oversee the development of predictive models, recommendation systems, and other advanced analytics solutions.
- Ensure the deployment of scalable and reliable machine learning models in production environments.
- Collaborate with product, engineering, and business teams to deliver data-driven solutions that create value.
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Collaboration and Stakeholder Engagement
- Partner with business leaders, product managers, and engineering teams to understand business requirements and define data science use cases.
- Act as a key point of contact for data science within the organization, advocating for data-driven decision-making.
- Communicate complex data science concepts to non-technical stakeholders, providing actionable insights and recommendations.
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Data Governance and Quality
- Establish best practices for data management, ensuring data quality, security, and compliance with relevant regulations.
- Work closely with data engineering teams to develop robust data pipelines, ensuring the availability of clean, reliable data for analysis.
- Implement ethical guidelines and governance frameworks to ensure responsible use of data and AI technologies.
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Project and Resource Management
- Oversee the planning, execution, and delivery of data science projects, ensuring that they are completed on time and within budget.
- Prioritize and manage multiple projects simultaneously, balancing short-term and long-term goals.
- Allocate resources effectively, ensuring that data science initiatives are aligned with the company’s strategic priorities.
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Research and Innovation
- Stay updated on the latest trends, tools, and technologies in data science, machine learning, and AI.
- Lead research efforts to explore new data science methodologies, tools, and frameworks that can enhance business performance.
- Drive proof-of-concept (Po C) projects to validate new models, techniques, and approaches.
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Measuring and Reporting Impact
- Develop KPIs to measure the impact of data science initiatives on business outcomes.
- Present results and findings to executive leadership, showcasing the value created through data science.
- Continuously evaluate the effectiveness of data models and solutions, driving improvements and optimization where necessary.
- Experience in data science, machine learning, and advanced analytics, with a proven track record of delivering impactful data solutions.
- Minimum 5 years leadership experience in managing data science teams, including setting strategic direction and mentoring junior team members.
- Strong experience with statistical modeling, machine learning algorithms, and data visualization tools.
- Technical Expertise: R Python, Spark, Scala, SQL, Cloud (AWS / Azure / GCP), BERT, NLP, Chat GPT, Llama 2 & 3, Gemini 1.5 pro, ML frameworks - Tensor Flow, Py Torch, CI/CD pipelines, Docker, Kubernetes, Lang Chain or Llama Index, Vector Database, CNN, RNN, LSTM
- Experience with big data technologies and platforms (e.g., Hadoop, Spark) and cloud environments (e.g., AWS, Google Cloud, Azure).
- Expertise in designing and implementing predictive models, natural language processing (NLP), and computer vision.
- Leadership Skills: Excellent leadership and people management skills, with the ability to inspire and grow a team.
- Strong communication and collaboration skills, with the ability to translate complex data science concepts into business terms.
- Proven ability to manage cross-functional teams and work in a matrixed environment.
- Business Acumen: Strong understanding of business strategy and the ability to align data science initiatives with key business goals.
- Proven ability to identify business opportunities through data and translate them into actionable data science solutions.
- Track record of influencing decision-making at the executive level through data-driven insights.
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