Data Scientist

Nielsen

Bengaluru

On-site

INR 2,500,000 - 4,500,000

Full time

14 days+

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Job summary

Nielsen Bengaluru is seeking a senior Data Scientist to lead end-to-end ML projects for media and audience measurement. You will mentor juniors, collaborate with data engineers and visualization specialists, and apply advanced models and DL/Generative AI.

Required 3–10+ years of ML experience, strong Python/Java/C++, and familiarity with Hadoop/Spark; Master's or PhD preferred.

Qualifications

  • 3–10+ years of experience building ML models for business use (IC).
  • Managers require 10+ years.
  • Proficient in Java, C++, Python and modeling tools (R, scikit-learn, Spark MLLib, MxNet, TensorFlow, NumPy, SciPy).
  • Familiar with large-scale distributed systems (Hadoop, Spark).
  • Master's degree or PhD in quantitative field.

Responsibilities

  • Lead data science projects from problem definition to deployment.
  • Design advanced analytical solutions including ML models.
  • Mentor data scientists and conduct code reviews.
  • Stay current with DS advances and apply new techniques.
  • Collaborate with Data Engineers for data quality and with Visualization specialist.
  • Develop audience segmentation and personalization algorithms.
  • Design studies to measure ad effectiveness and ROI.
  • Create predictive models for audience behavior and trends.
  • Apply causal inference methods to establish causality.

Job description

Responsibilities:
  • Project Leadership: Lead data science projects, guiding junior team members from problem definition to model deployment.
  • Solution Design and Development: Design and implement advanced analytical solutions, including ML models and statistical frameworks, for media and audience measurement.
  • Mentorship and Knowledge Transfer: Mentor Data Scientists, provide technical guidance, conduct code reviews, and foster continuous learning.
  • Research and Innovation: Stay current with data science advancements, applying new techniques to improve models, develop solutions, and uncover insights.
  • Collaboration: Work closely with Data Engineers to ensure data availability and quality, and with the Data Visualisation Specialist to effectively communicate findings. Core Expertise:
  • Audience Segmentation and Profiling: Develop sophisticated audience segments by analysing demographics, behaviours, interests, and consumption patterns across diverse media platforms.
  • Content Optimisation and Personalisation: Analyse content performance to identify engagement drivers and build recommendation engines or personalisation algorithms.
  • Ad Effectiveness and Measurement: Design and execute studies to measure the impact of advertising campaigns, including attribution modelling, lift analysis, and ROI calculation.
  • Predictive Analytics and Forecasting: Create predictive models for audience behaviour, future trends, content virality, or advertising spend.
  • Causal Inference: Apply experimental design (A/B testing, multivariate testing) and quasi-experimental methods to establish causality between interventions and outcomes.
Requirements:
  • Experience Level: For Individual contributors, 3‑10+ years of experience building machine learning models for business applications (hiring at all levels). For Managers: 10+ years of experience.
  • Proficiency in: Programming languages (Java, C++, Python) and modelling tools (R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy).
  • Familiarity with: Large‑scale distributed systems (Hadoop, Spark).
  • Education: Master's degree in mathematics, graph theory, operations research, statistics, engineering, or a related quantitative discipline, or a PhD.
  • 4‑10+ years of experience building machine learning models for business applications.
  • The ideal candidate is proficient in programming languages (Java, C++, Python), experienced with modelling tools (R, scikit‑learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy), and familiar with large‑scale distributed systems (Hadoop, Spark).
  • Master's degree in mathematics, statistics, engineering, or a related quantitative discipline, or a PhD.
  • Core DS Skills: Have a strong understanding of core data science algorithms and their applications. Experience with Deep Learning (DL) and Generative AI is desirable.
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