- Company Overview: At Bridgenext, we engineer Growth Operating Systems. Most enterprises have spent millions on their revenue stack and still aren't seeing the growth they expected.They have tools that function, but no system that wins. We help growth-hungry companies close that gap by turning fragmented platforms, siloed teams, and disconnected data into integrated Growth OS. More than a technology company or marketing agency, we're a global digital consultancy with experts in engineering, data, AI, creative and more.
Our teams are made up of experts who believe in engineering impact, starting with putting people at the center of everything we do. Every team member directly shapes our work, culture, and values. Nothing matters more to us than a kind, respectful, fulfilling environment that supports everyone. Our flexible, inclusive culture gives you the autonomy, resources, and opportunities to thrive
- Position Description: As a Data Science/Machine Learning Developer, you will be responsible for designing, developing, implementing, and optimizing machine learning models and data-driven solutions. You will work closely with cross-functional teams, including data engineers, software developers, architects, analysts, and business stakeholders, to translate business problems into scalable machine learning solutions.
The ideal candidate will have strong hands-on experience with Python, PySpark, Machine Learning, and Microsoft Azure, particularly Azure Machine Learning. Experience with Microsoft Fabric, Azure Synapse Analytics, MLOps, and Generative AI is an added advantage.
Must Have Skills:
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Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative discipline.
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Proven experience as a Data Scientist, Machine Learning Developer, ML Engineer, or similar role.
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Minimum 4 years of experience developing Data Science and Machine Learning solutions.
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Strong proficiency in Python for data analysis, machine learning, and model development.
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Strong understanding of statistics, machine learning concepts, and data science fundamentals.
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Experience with machine learning frameworks and libraries such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or LightGBM.
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Strong experience with data preprocessing and analytical libraries such as Pandas and NumPy.
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In-depth understanding of machine learning techniques including regression, classification, clustering, anomaly detection, recommendation, forecasting, and neural networks.
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Experience performing Exploratory Data Analysis (EDA), feature engineering, feature selection, data cleansing, and data preparation.
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Experience evaluating and optimizing models for accuracy, performance, scalability, and business effectiveness.
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Experience using Python and PySpark for data manipulation, analysis, feature engineering, and model development.
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Hands-on experience with Azure Machine Learning for model development, experimentation, deployment, and monitoring.
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Strong working knowledge of Microsoft Azure and cloud-based data and machine learning solutions.
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Experience designing, developing, and deploying machine learning models to solve complex business problems.
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Experience converting experimental models and notebooks into maintainable, production-ready solutions.
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Understanding of model validation, cross-validation, hyperparameter tuning, model explainability, and performance metrics.
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Hands-on experience leveraging AI-assisted development tools such as GitHub Copilot, Claude Code, or equivalent tools for software development, code generation, debugging, refactoring, unit testing, test automation, code review, and technical documentation. Candidates should be able to effectively use AI tools throughout the development and testing lifecycle while validating generated code and maintaining engineering quality, security, and coding standards.
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Participate in architecture, design, and code reviews while establishing and maintaining engineering and data science best practices.
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Collaborate with data engineers, analysts, architects, and business stakeholders to understand requirements and translate them into technical solutions.
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Develop and maintain documentation covering models, datasets, experiments, assumptions, evaluation results, and deployment processes.
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Experience with Git and modern software development practices.
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Ability to stay current with advancements in machine learning, data science, cloud platforms, and related technologies.
Preferred Skills:
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Experience with Microsoft Fabric and its data engineering/data science capabilities.
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Experience with Azure Synapse Analytics.
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Experience with Azure Databricks and large-scale data processing.
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Knowledge of SQL and experience working with relational databases and data warehouses.
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Experience with MLOps practices, including model versioning, deployment, monitoring, retraining, and lifecycle management.
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Experience with MLflow or similar model and experiment management tools.
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Familiarity with deep learning techniques such as CNNs, RNNs, and Transformer architectures.
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Experience with TensorFlow or PyTorch for deep learning use cases.
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Understanding of data lakes, data pipelines, distributed data processing, and cloud-based analytical platforms.
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Familiarity with Generative AI, Large Language Models (LLMs), Azure OpenAI, or RAG is an added advantage but is not a core requirement.
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Familiarity with AWS or Google Cloud Platform is an added advantage.
Professional Skills:
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Strong analytical and problem-solving capabilities with the ability to translate business problems into data science solutions.
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Ability to evaluate different modeling approaches and clearly explain technical decisions and trade-offs.
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Experience discussing technical approaches with clients and recommending appropriate solutions.
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Ability to communicate model results and technical concepts effectively to both technical and non-technical stakeholders.
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Ability to work effectively in a cross-functional team as well as independently.
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Self-starter with the ability to take ownership of assigned solutions from analysis through implementation.
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Strong verbal and written communication skills.
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Strong time-management and prioritization skills with the ability to work effectively under delivery deadlines.
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Ability to mentor junior developers and contribute to team-level data science and engineering best practices.
Professional Skills:
Solid written, verbal, and presentation communication skills
Strong team and individual player
Maintains composure during all types of situations and is collaborative by nature
High standards of professionalism, consistently producing high quality results
Self-sufficient, independent requiring very little supervision or intervention
Demonstrate flexibility and openness to bring creative solutions to address issues
Bridgenext is an Equal Opportunity Employer
