Freelance Data Scientist Machine Learning Jobs (NOW HIRING)

We are seeking a highly motivated Data Scientist / Machine Learning Engineer to join a team focused on building advanced analytics and Generative AI (Gen AI) solutions for multiple Lines of Business (LOBs) within Corporate & Commercial Investment Banking (CCIB). This role combines strong data analysis capabilities with machine learning and emerging Gen AI techniques to drive business insights, automation, and innovation.

The ideal candidate is hands-on, analytical, and comfortable owning the full lifecycle of data science solutions—from problem definition through model development and deployment—while collaborating closely with engineering and business stakeholders.

The candidate will perform in-depth data analysis and exploration using SQL and statistical techniques to uncover patterns, solve business problems, and support data-driven decision-making. This includes working with large, complex datasets and ensuring data quality, integrity, and usability.

They will design, develop, and implement scalable solutions using Python or Java, leveraging standard data science and machine learning libraries such as NumPy, SciPy, Matplotlib, and Scikit-learn. Responsibilities include building reusable pipelines for data processing, feature engineering, and model evaluation.

The role involves developing and evaluating machine learning models, including tree-based and ensemble algorithms such as Random Forest and XGBoost. The candidate will assess model performance, tune hyperparameters, and ensure models meet business and technical requirements.

A key component of this position is applying AI-assisted techniques to enhance productivity and insights. This includes crafting effective prompts using Gemini or similar generative AI models to accelerate tasks such as data exploration, feature generation, analysis, and summarization of findings.

The candidate will communicate insights clearly through visualizations, reports, and presentations, translating complex technical outputs into actionable business recommendations. They will partner closely with engineering teams for implementation and with business stakeholders to ensure alignment with strategic goals.

Candidates must possess strong SQL and data analysis skills, with the ability to work efficiently across structured and semi-structured datasets. Proficiency in Python or Java is required, particularly for data science, machine learning, and analytical workloads.

Hands-on experience with machine learning frameworks and model development is essential, including experience building, training, and evaluating predictive models in production or near-production environments.

The candidate should demonstrate the ability to work independently and own initiatives end-to-end, from problem definition and requirements gathering through solution delivery and validation.

Experience using generative AI models to augment analytical workflows is required. This includes familiarity with prompt engineering, leveraging LLMs for automation, and integrating Gen AI capabilities into analytical processes.

The team develops and deploys Gen AI products and solutions across multiple business functions within CCIB. These solutions aim to enhance productivity, automate workflows, and unlock new business value through AI-driven insights.

While deep expertise in Gen AI is not mandatory, candidates are expected to have foundational exposure to Gen AI concepts, tools, and use cases. Typical experience ranges from 1–3 years and may include working with large language models (LLMs), prompt engineering, or AI-assisted analytics.

Candidates should demonstrate a strong interest in emerging AI technologies and a willingness to continuously learn and apply Gen AI innovations in practical business contexts.

Experience working in financial services, banking, or capital markets environments is preferred, particularly in data-driven or risk-focused domains.

Familiarity with cloud platforms, data engineering pipelines, and model deployment frameworks is a plus.

Exposure to big data technologies, distributed computing, or real-time analytics environments is also beneficial.

The ideal candidate is a self-driven data professional who combines strong analytical thinking with practical machine learning and emerging AI capabilities. They are comfortable navigating ambiguous problems, translating business needs into technical solutions, and delivering measurable outcomes.

They thrive in a collaborative environment, communicate effectively with both technical and non-technical stakeholders, and are eager to apply both traditional machine learning and modern Gen AI techniques to solve complex business challenges.

Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.