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    Job · 21174603

    Anaplan logo

    Senior Technical Lead - Data Science

    Anaplan · Gurugram · onsite · Posted 66d ago

    Undisclosed15–20 yrsfulltimeSourced fromIIM Jobs

    About a minute. You'll make an account on the way through.

    About this role

    Company Overview:

    Anaplan is a transformative cloud-native enterprise platform that enables organizations to orchestrate business performance across finance, sales, supply chain, and operations. By connecting data, people, and plans in real-time, Anaplan empowers global enterprises to navigate complex market dynamics and make informed, data-driven decisions. Headquartered in San Francisco with a significant presence in Gurugram, the company serves thousands of the worlds largest organizations, providing the agility needed to thrive in an increasingly volatile business landscape.

    Role Overview:

    As a Senior Technical Lead in Data Science, you will spearhead the development of advanced analytical models that drive the intelligence behind our planning platform. You will operate at the intersection of complex data architecture and business strategy, collaborating with cross-functional engineering and product teams to translate ambiguous business challenges into scalable machine learning solutions. This role is pivotal in shaping the future of our predictive capabilities, directly influencing how our global clients forecast outcomes and optimize their operational efficiency.

    Key Responsibilities:

    - Architect and deploy sophisticated machine learning models that integrate seamlessly into the Anaplan ecosystem to enhance predictive accuracy for enterprise users.

    - Lead the end-to-end lifecycle of data science projects, from initial data exploration and feature engineering to model training, validation, and production deployment.

    - Mentor a team of high-performing data scientists, fostering a culture of technical excellence, rigorous code reviews, and innovative problem-solving.

    - Collaborate with product managers and stakeholders to identify high-impact use cases for time series forecasting and predictive modeling that provide a competitive edge to our customers.

    - Optimize model performance and scalability by leveraging deep learning frameworks to handle large-scale, high-dimensional datasets.

    Required Skillset:

    - Demonstrated expertise in building and deploying production-grade machine learning models using Python, with deep proficiency in libraries such as Scikit-Learn, Numpy, and Pandas.

    - Proven ability to design and implement complex deep learning architectures using Tensorflow or PyTorch to solve real-world business problems.

    - Strong command over time series forecasting techniques and predictive modeling, with a track record of applying these to large-scale enterprise data.

    - Exceptional communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders and influence executive decision-making.

    - A minimum of 15 - 20 years of professional experience in data science, with a strong background in leading technical teams and delivering high-impact analytical products.

    - Ability to thrive in a hybrid work environment in Gurugram, demonstrating strong interpersonal skills and the capacity to lead distributed teams effectively.

    - A Masters degree or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field from a premier institute is preferred.

    Sourced from iimjobs

    Skills

    PhDData ManagementData ScienceArtificial IntelligenceMachine Learning

    About Anaplan

    Anaplan logo

    Anaplan

    IT Services & Consulting

    45

    employees

    San Francisco, California

    USA

    About

    Anaplan operates within the business software sector, facilitating agility in decision-making through advanced modeling and forecasting capabilities for various teams.

    Industries

    IT Services & Consulting

    Employee ratings

    9 reviews

    3.1/ 5

    Culture

    3.3

    Career growth

    3.2

    Work-life

    3.4

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