<p><p><b>Job Description :</b></p><p><br/></p><p>We are seeking a seasoned and highly analytical Data Scientist with 5-7 years of professional experience to join our dynamic data science team.
</p><p><br/></p><p>The ideal candidate will be a technical expert and a strategic thinker, capable of turning complex datasets into actionable insights and robust predictive models.
</p><p><br/></p><p>You will play a key role in the entire data science lifecycle, from problem definition and data exploration to model development, validation, and deployment.
</p><p><br/></p><p>Expertise in both traditional statistical modeling and modern deep learning techniques, including Generative AI, is highly valued.<br/><br/><b>Key Responsibilities Solving & Strategy :</b></p><p><br/></p>- Collaborate with stakeholders across business units (e.g., Product, Marketing, Operations) to identify key business questions and formulate data-driven solutions.<br/><br/></p><p>- Translate business needs into a clear, structured data science problem and define the project scope, objectives, and success metrics.<br/><br/></p><p>- Drive strategic initiatives by providing data-backed recommendations to senior Analysis & Modeling :</b></p><p><br/></p>- Perform exploratory data analysis (EDA), data cleaning, and feature engineering to prepare data for modeling.<br/><br/></p><p>- Develop, implement, and validate machine learning models (e.g., classification, regression, clustering, time series analysis) to solve complex business problems.<br/><br/></p><p>- Design and conduct experiments (A/B testing) to measure the impact of new features or AI & Advanced Techniques :</b></p><p><br/></p>- Research, evaluate, and apply advanced deep learning techniques, including Generative AI models (e.g., Large Language Models, Diffusion Models), to create innovative solutions.<br/><br/></p><p>- Develop and implement strategies for prompt engineering, Retrieval Augmented Generation (RAG), and fine-tuning models to enhance performance and contextual relevance.<br/><br/></p><p>- Stay current with the latest research and trends in AI and machine learning, and assess their applicability to our business & Collaboration :</b></p><p><br/></p>- Clearly communicate findings, methodologies, and the impact of models to both technical and non-technical audiences through reports, dashboards, and presentations.<br/><br/></p><p>- Partner with Data Engineers to build scalable data pipelines and with MLOps Engineers to deploy models into production.<br/><br/></p><p>- Mentor junior data scientists and contribute to a culture of continuous learning and data literacy within the & Tooling :</b></p><p><br/></p>- Write production-quality code in Python for data analysis, modeling, and automation.<br/><br/></p><p>- Utilize version control systems (e.g., Git) and collaborate effectively on shared codebases.<br/><br/></p><p>- Proficiency with relevant data science tools and platforms (e.g., SQL, cloud platforms like AWS, Azure, Qualifications : :</b> Master's or Ph.D. in a quantitative field such as Computer Science, Statistics, Mathematics, Physics, or a related :</b> 5-7 years of hands-on experience as a Data Scientist or in a similar role, with a strong portfolio of projects that have been successfully deployed and delivered measurable business Skills :</b> Expert proficiency in Python and its data science ecosystem (Pandas, NumPy, SciPy, Learning :</b> Deep understanding of a wide range of ML algorithms and statistical modeling Learning :</b> Hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and an understanding of neural network AI :</b> Practical experience with Large Language Models (LLMs), prompt engineering, and fine-tuning.
Familiarity with other generative models is a Querying :</b> Strong SQL skills are a :</b> Proven ability to define and solve complex, ambiguous problems with an analytical and methodical :</b> Excellent written and verbal communication skills, with the ability to present complex technical concepts clearly and concisely to diverse Qualifications (Bonus Points) :</b></p><p><br/></p>- Experience with MLOps principles and tools (e.g., MLflow, Kubeflow).<br/><br/></p><p>- Proficiency with cloud-based data warehouses and services (e.g., Snowflake, BigQuery, Redshift, S3).<br/><br/></p><p>- Experience with distributed computing frameworks (e.g., Spark, Dask).<br/><br/></p><p>- Experience in [specific industry, e.g., FinTech, Healthcare, E-commerce, SaaS].<br/><br/></p><p>- Publications in peer-reviewed journals or major conferences.</p><br/></p> (ref:hirist.tech)