Job Description & How to Apply Below
Cybage Software is a technology consulting organization specializing in outsourced product engineering services. Our unique offerings span the technological spectrum–from cutting-edge software development to transformative digital strategies. In 1995, Cybage was founded with a mission to revolutionize the product engineering landscape, empowering businesses to soar above their limitations. What commenced as a modest venture has transformed into a pioneering force, shaping the digital future with innovative solutions tailored to our clients' unique needs.
Job Description
Key Responsibilities:
Analyze historical video survey data (Purchase Intent, Brand Favourability, Facial Coding metrics, etc.) to identify performance drivers.
Develop predictive models that can forecast ad performance metrics based on creative features, audience segments, and historical trends.
Apply statistical, machine learning, and deep learning techniques to extract insights and build scalable prediction pipelines.
Collaborate with business stakeholders to translate requirements into technical solutions.
Design validation frameworks to benchmark predictive performance against actual survey outcomes.
Build dashboards/visualizations for business teams to interpret predictive outcomes.
Continuously refine and improve model accuracy by incorporating new data sources and feedback loops.
Required
Skills & Experience:
Bachelor’s/Master’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, or related fields .
10+ years of experience in machine learning / predictive analytics / data science .
Strong proficiency in Python/R and ML libraries (e.g., scikit-learn, Tensor Flow, PyTorch, XGBoost).
Hands-on experience with predictive modeling, regression, classification, and time-series forecasting .
Experience in feature engineering from unstructured data (e.g., video metadata, textual content, tags).
Familiarity with media/advertising analytics (ad effectiveness, consumer insights, brand lift studies) is a strong plus.
Strong SQL knowledge and experience with large datasets (10k+ records, multiple metrics) .
Experience with computer vision (video/image analysis) for extracting features from creatives.
Excellent communication skills to explain technical results to non-technical stakeholders.
Nice to Have:
Knowledge of cloud platforms (AWS) for model deployment and scaling.
Exposure to natural language processing (NLP) for analyzing ad scripts, captions, or metadata.
Experience with facial coding / emotion AI frameworks .
Previous experience in adtech, martech, or consumer research domains.
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