Senior Data Scientist - Machine Learning Models

About the job : Job Description :

At Synoptek, we think globally and act locally.

As the world's first Managed Experience Provider (MxP), Synoptek provides world-class strategic IT leadership and hyper-efficient IT operational support, enabling our global client base to grow and transform their businesses.

We are excited to have experienced continuous growth and, in keeping with that momentum, we are seeking to add talent to our team.

When you partner with Synoptek, you engage with an ever-growing, ever-evolving IT organization that provides a high-caliber team, drives growth, and delivers clarity.

This is an exciting opportunity for you to be a part of this journey!

The Senior Data Scientist is responsible for leading advanced analytics, machine learning, and data-driven solution development initiatives aligned with organizational business objectives and operational transformation strategies.

This role collaborates with product teams, engineering teams, and business stakeholders to design predictive models, forecasting solutions, optimization frameworks, and enterprise data science strategies that support decision-making, operational efficiency, and measurable business impact across enterprise environments.

Mandatory Skills :

• Data Science, Python, SQL, PyTorch, MLOps, Model Deployment, Model Monitoring, Unstructured Data Analysis, Cloud Platforms, AWS, Azure, Generative AI, Agentic AI, Retrieval Augmented Generation, lang graph.

Role :

• Lead end-to-end data science initiatives including data acquisition, exploratory analysis, feature engineering, model development, validation, deployment, monitoring, and optimization activities.

• Design, develop, and deploy predictive models, forecasting algorithms, machine learning frameworks, and statistical models in production environments.

• Collaborate with business stakeholders, product teams, and engineering teams to define analytical strategies, business requirements, and data-driven solution approaches.

• Identify, analyze, and utilize structured and unstructured data sources to support enterprise analytics and operational initiatives.

• Perform data wrangling, preprocessing, cleansing, transformation, and feature engineering activities to improve model performance and analytical accuracy.

• Develop scalable machine learning and optimization solutions using Python, R, SQL, and data science frameworks including scikit-learn, TensorFlow, PyTorch, XGBoost, and Statsmodels.

• Support big data processing and distributed computing initiatives using Hadoop, Spark, Hive, and related technologies.

• Create dashboards, visualizations, and analytical reporting solutions using Tableau, Power BI, Plotly, Dash, and other visualization tools.