unifyCX - Data Scientist - Machine Learning (10-15 yrs)

Title of the Position : Data Scientist - Machine Learning

Designation : Senior Data Scientist

Work Location : Remote

Business Unit : Engineering (Lightbird)

Shifts : General (candidate should be flexible)

Who We Are :

unifyCX is an emerging Global Business Process Outsourcing company with a strong presence in the U.S., Colombia, Dominican Republic, India, Jamaica, Honduras, and the Philippines. We provide personalized contact centers, business processing, and technology outsourcing solutions to clients worldwide. In nearly two decades, unifyCX has grown from a small team to a global organization with staff members all over the world dedicated to supporting our international clientele.

- At unifyCX, we leverage advanced AI technologies to elevate the customer experience (CX) and drive operational efficiency for our clients. Our commitment to innovation positions us as a trusted partner, enabling businesses across industries to meet the evolving demands of a global market with agility and precision.

- unifyCX is a certified minority-owned business and an EOE employer who welcomes diversity.

Overview :

The AI Engineering team at UnifyCX is looking for a motivated, challenge driven full stack Senior Data Scientist- Machine Learning to join us in building our next-gen omnichannel CX delivery and process automation platform Lightbird.

Roles and Responsibilities :

1. Innovate with AI

- Design, train, and optimize domain-aware ML models using both classical ML (e.g., boosting, bagging) and deep learning (e.g., Transformers).

- Focus on language and audio modalities, incorporating multi-modal approaches (text, voice, context) to deliver robust, guardrailed AI copilots.

2. Real-World Data :

- Devise strategies to generate high-quality, domain-specific training data for cutting-edge paradigms (e.g., transfer learning, RLHF), ensuring balanced coverage and effective labeling.

- Uncover insights from large enterprise datasets, applying unsupervised approaches to automate high-impact AI services.

3. Model Performance :

- Benchmark models for accuracy, efficiency, and scalability. Develop dashboards to track key metrics, apply interpretability methods and proactively mitigate model drift.

4. Data Analytics :

- Analyze large-scale text, speech, and time-series data to surface insights for strategic decision-making.

- Employ data visualization tools (Tableau, Power BI) and advanced statistical methods (forecasting, survival analysis) to communicate findings.

5. Research & Innovation :

- Prototype and experiment with emerging ML algorithms to continuously refine capabilities.

- Stay current with leading research, run proof-of-concept projects, and share knowledge through presentations and white papers.

6. Thrive in Agile Environments :

- Work cross-functionally with ML engineers, software engineers, and product managers to drive new AI-powered product initiatives.

- Engage in sprints, code reviews, and iterative development to maintain quality and accelerate delivery.

- Maintain clear, comprehensive documentation and uphold strong engineering standards.

Qualifications :

Required :

- Masters degree in Data Science, Computer Science, Applied Math, Statistics, Physics, or a related field.

- 5+ years post Masters / 2+ years post Ph.D / 7+ years industry experience or equivalent in applied machine learning or data science with strong background in language/ audio/image/ time series models.

Technical Skills :

- Advanced proficiency in Python and building ML solutions in a standard library such as PyTorch or TensorFlow.

- Proficiency with SQL or similar querying languages; familiarity in dashboarding tools (Tableau, Power BI) for reporting.

- Strong understanding of deep learning architectural fundamentals, Transformers and natural language processing techniques.

- Demonstrable production grade experience with Transformers, RNN architectures, and text embedding methods for language/speech applications.

- Training Data Synthesis : Proven track record of creating domain-specific datasets, employing augmentation strategies, and mitigating biases.

- Advanced Training Paradigms : Knowledge of transfer learning, Zero-Shot Reinforcement Learning from Human Feedback (ZRLHF), and few/zero-shot modeling.

Tools :

- Experience with software engineering in production grade systems- ability to design, build, version control, test, and maintain software applications that are used by customers or end users.

- Familiarity with version control systems (Git) and project management tools (JIRA, Asana).

Soft Skills :

- Excellent problem-solving abilities, strong communication skills, and the ability to work both independently and collaboratively in a team environment.

Preferred :

- Advanced Degree : PhD in Computer Science, ML, or related fields.

- Regulated Industries : Industry experience in high-compliance sectors (e.g., healthcare, finance, cybersecurity), including custom ML model development and familiarity with the unique challenges of regulated environments is highly preferred!

- Project Management : Comfortable with JIRA, Asana, or similar platforms for sprint planning and backlog tracking.

Brownie Points for :

- AI Guardrails : Background in designing robust ethical guardrails (e.g., bias detection, content filtering) for responsible AI deployment. Experience with guided generation and model alignment, and privacy preserving safe AI techniques in training and inference.

- Familiarity with classical applied ML techniques like boosting (XGBoost, LightGBM), bagging(Random Forest) and time series analysis.

- Open Source & Publications : Contributions to open-source projects or academic publications are highly welcome. We would love to see a showcase of your skills in past projects and PoCs

- Distributed Systems : Experience with distributed machine learning/ large-scale model deployments.

- Real-Time Processing : Background in real-time audio processing and streaming technologies

- AI Guardrails : Background in designing robust ethical guardrails (e.g., bias detection, content filtering) for responsible AI deployment