Develop and implement personification algorithms to enhance user experience, and collaborate with cross-functional teams to integrate personalized features into the platform.
Work closely with product and business teams to devise data-driven solutions and statistical models.
Collaborate on proof of concepts and experiments to showcase the value that machine learning can contribute, aligning with product requirements.
Constantly monitor the business and product metrics and fine-tune the existing models according to the changing market trends and needs.
Recognize, assess, and interpret trends or patterns within extensive and complex data sets.
Utilize these insights to generate actionable recommendations for the improvement and advancement of both product features and overall business strategies.
Build predictive models to forecast user behaviors, demand, and market trends.
Implement models to optimize pricing strategies, cataloguing, and recommendations on different touch points of the user journey.
Collaborate with engineering teams to deploy scalable and reproducible data science models.
Define appropriate testing environments, including A/B testing methodologies, and establish accurate metrics for tracking new features and product performance.
Collaborate with the analytics team to construct precise measurement frameworks to evaluate outcomes effectively.

Requirements

B. Tech. / BE - Computer, IT, Electronics (Circuit Branches) only, Bachelors/Masters in Data-Science/ML or a similar stream.
Strong hands-on working experience in Python and SQL.
2 to 4 years of experience in developing predictive models through the application of statistical, machine learning, and deep learning algorithms.
Ability to think creatively to solve complex business problems using data-driven solutions should have good problem-solving and strong analytical skills.
Fair understanding of different databases, and stream processing tools - Kafka, queues.
Well-versed with Linux fundamentals and should be able to understand basic performance metrics such as CPU, memory, and network latency.
Hands-on experience with frameworks like Flask, fast API, Django, Numpy, pandas, etc.
Familiarity with analytical tools such as Tableau, and Excel is an added advantage.
Knowledge of cloud-based (preferably AWS) solutions such as lambda, SQS, EKS, etc.

(ref:hirist.tech)

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