• Own and evolve the enterprise reference architecture for secure, scalable OT-to-IT data integration across plant systems, DMZ layers, and the Unified Data Platform • Provide architectural oversight to ensure all integrations align with enterprise design principles and future analytics/AI needs. • Define and govern enterprise standards for OT data integration, including protocols, data models, security, and data quality. • Create reusable onboarding patterns, templates, and playbooks to enable consistent, scalable integration across sites. • Define and operate defensible architectures for OT data egress: OPC UA servers, MQTT/AMQP brokers, data diodes/one-way gateways, and DMZ nodes; prohibit direct control-network access. • Design store-and-forward and buffering strategies to protect control systems and ensure data continuity during outages. • Build/oversee extraction from PHD/PI historians, DCS/PLC/SCADA, LIMS, and lab instruments; implement event frames (batches, phases, grade changes), alarm/event streams, and equipment hierarchies (asset models/AF). • Condition signals (resampling, dead-banding, interpolation, outlier/sensor validation), apply units/calibration metadata, and align to production calendars and ISA-88/95 structures. • Implement observability: freshness, gap detection, tag quality/availability, schema drift; build dashboards and alerts; own on-call runbooks and incident response for OT data flows. • Enforce ISA-62443/ISA-99 controls with Cyber/IT (RBAC, credential vaulting, patching, vulnerability remediation). • Partner with plant controls, process engineering, Reliability/Maintenance, and HSE to prioritize tags, events, and KPIs. • Work with the Manager, Unified Data Platform & AI Engineering to land curated OT data with lineage/DQ and with the Reporting & BI team to certify datasets. • Act as the single thread leader for OT data integration across plants. Drive roadmap and prioritization across OT, IT, and business stakeholders.

Role Description Aaruush, an AI-focused organisation, is looking for an experienced Data Scientist to join its team. We would like an individual who is passionate about analyzing data, understanding trends and deriving insights from the data. The ideal candidate should have an inherent curiosity to explore, mine and interpret data to drive meaningful action. - Leverage advanced methods from predictive modeling, machine learning, statistical analysis, and other quantitative data analytics practices to answer business questions. - Use SQL to extract, review, and manipulate data from databases and automated processes. - Work closely with Product and Technology teams to ensure optimal data architecture processes. - Design and maintain comprehensive reporting and models to conduct data-driven decisions. - Coordinate with internal stakeholders to ensure data integrity and accuracy. - Develop strategies to reduce data complexity and optimize ETL processes. - Create and present performance metrics reports across the organisation. Qualifications - Bachelor's degree or higher in Data Science, Statistics, or similar quantitative discipline - 5+ years of relevant data analysis and experience in data science - Expert level knowledge of statistical modeling libraries such as taR, SciPy, and scikit-learn - Experience with relational databases such as SAP HANA, Oracle, and MySQL - Proficiency with scripting language (Python) and scripting libraries such as NumPy and Pandas - Excellent communication, problem-solving and visualisation skills Company Description

• Turn transaction and behavioral data from SoLo Score, SoLo Wallet, SoLo Marketplace, Borrow, and Lend into signals the business can act on. • Keep lending risk and fraud detection sharp in real time. • Design the workflows, prompts, guardrails, and evaluation standards that let agents run production analysis, model monitoring, pipeline maintenance, and anomaly detection at scale. • Own data quality, governance, and reliability end to end. • Translate ambiguous business questions into clear, defensible answers for leadership. • Build and refine the evaluation harnesses that keep agent output accurate and auditable. • Monitor SoLo Score performance, default and delinquency trends, and fraud signals, intervening when human judgment has to lead. • Stand up dashboards and BI that give the team a live view of marketplace health. • Partner with product and risk to ship data-informed decisions fast.

• Apply statistical, machine learning, and econometric methods to large datasets to evaluate channel performance and isolate the causal impact of marketing and sales activity on a multi-touch B2B sales cycle • Build attribution models and investment frameworks that inform Spellbook's channel spend, helping marketing and finance understand which messages resonate with which segments at each stage of the funnel • Partner with Marketing Ops, Sales, and Engineering to unify data across first- and third-party sources, so every growth decision is made with full context • Drive experimental design across acquisition, activation, and retention surfaces, enabling fast, low-cost iteration on new channels and messaging • Shape the data culture at Spellbook by improving the tools, processes, and systems the broader team uses to make decisions at scale